diff --git a/results/predict_modality/data/dataset_info.json b/results/predict_modality/data/dataset_info.json
index 93c45c55..9a0b40c0 100644
--- a/results/predict_modality/data/dataset_info.json
+++ b/results/predict_modality/data/dataset_info.json
@@ -1,25 +1,14 @@
 [
   {
-    "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
-    "dataset_name": "NeurIPS2021 CITE-Seq (GEX2ADT)",
-    "dataset_summary": "Single-cell CITE-Seq (GEX+ADT) data collected from bone marrow mononuclear cells of 12 healthy human donors.",
-    "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
-    "data_reference": "luecken2021neurips",
-    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122",
-    "date_created": "25-11-2024",
-    "file_size": 704994,
-    "common_dataset_id": "openproblems_neurips2021/bmmc_cite"
-  },
-  {
-    "dataset_id": "openproblems_neurips2021/bmmc_multiome/normal",
-    "dataset_name": "NeurIPS2021 Multiome (GEX2ATAC)",
+    "dataset_id": "openproblems_neurips2022/pbmc_multiome/swap",
+    "dataset_name": "OpenProblems NeurIPS2022 Multiome (ATAC2GEX)",
     "dataset_summary": "Single-cell Multiome (GEX+ATAC) data collected from bone marrow mononuclear cells of 12 healthy human donors.",
-    "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
-    "data_reference": "luecken2021neurips",
-    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122",
-    "date_created": "25-11-2024",
-    "file_size": 31080807,
-    "common_dataset_id": "openproblems_neurips2021/bmmc_multiome"
+    "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
+    "data_reference": "lance2024predicting",
+    "data_url": "https://www.kaggle.com/competitions/open-problems-multimodal/data",
+    "date_created": "09-01-2025",
+    "file_size": 18717069,
+    "common_dataset_id": "openproblems_neurips2022/pbmc_multiome"
   },
   {
     "dataset_id": "openproblems_neurips2021/bmmc_multiome/swap",
@@ -28,10 +17,21 @@
     "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
     "data_reference": "luecken2021neurips",
     "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122",
-    "date_created": "25-11-2024",
+    "date_created": "09-01-2025",
     "file_size": 7883109,
     "common_dataset_id": "openproblems_neurips2021/bmmc_multiome"
   },
+  {
+    "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
+    "dataset_name": "NeurIPS2021 CITE-Seq (GEX2ADT)",
+    "dataset_summary": "Single-cell CITE-Seq (GEX+ADT) data collected from bone marrow mononuclear cells of 12 healthy human donors.",
+    "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
+    "data_reference": "luecken2021neurips",
+    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122",
+    "date_created": "09-01-2025",
+    "file_size": 704994,
+    "common_dataset_id": "openproblems_neurips2021/bmmc_cite"
+  },
   {
     "dataset_id": "openproblems_neurips2022/pbmc_cite/normal",
     "dataset_name": "OpenProblems NeurIPS2022 CITE-Seq (GEX2ADT)",
@@ -39,7 +39,7 @@
     "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
     "data_reference": "lance2024predicting",
     "data_url": "https://www.kaggle.com/competitions/open-problems-multimodal/data",
-    "date_created": "25-11-2024",
+    "date_created": "09-01-2025",
     "file_size": 591886,
     "common_dataset_id": "openproblems_neurips2022/pbmc_cite"
   },
@@ -50,7 +50,7 @@
     "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
     "data_reference": "lance2024predicting",
     "data_url": "https://www.kaggle.com/competitions/open-problems-multimodal/data",
-    "date_created": "25-11-2024",
+    "date_created": "09-01-2025",
     "file_size": 32551804,
     "common_dataset_id": "openproblems_neurips2022/pbmc_cite"
   },
@@ -61,8 +61,30 @@
     "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X 3 prime Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
     "data_reference": "luecken2021neurips",
     "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122",
-    "date_created": "25-11-2024",
+    "date_created": "09-01-2025",
     "file_size": 13467880,
     "common_dataset_id": "openproblems_neurips2021/bmmc_cite"
+  },
+  {
+    "dataset_id": "openproblems_neurips2022/pbmc_multiome/normal",
+    "dataset_name": "OpenProblems NeurIPS2022 Multiome (GEX2ATAC)",
+    "dataset_summary": "Single-cell Multiome (GEX+ATAC) data collected from bone marrow mononuclear cells of 12 healthy human donors.",
+    "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2022. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
+    "data_reference": "lance2024predicting",
+    "data_url": "https://www.kaggle.com/competitions/open-problems-multimodal/data",
+    "date_created": "09-01-2025",
+    "file_size": 4322721,
+    "common_dataset_id": "openproblems_neurips2022/pbmc_multiome"
+  },
+  {
+    "dataset_id": "openproblems_neurips2021/bmmc_multiome/normal",
+    "dataset_name": "NeurIPS2021 Multiome (GEX2ATAC)",
+    "dataset_summary": "Single-cell Multiome (GEX+ATAC) data collected from bone marrow mononuclear cells of 12 healthy human donors.",
+    "dataset_description": "Single-cell CITE-Seq data collected from bone marrow mononuclear cells of 12 healthy human donors using the 10X Multiome Gene Expression and Chromatin Accessibility kit. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site.",
+    "data_reference": "luecken2021neurips",
+    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122",
+    "date_created": "09-01-2025",
+    "file_size": 31080807,
+    "common_dataset_id": "openproblems_neurips2021/bmmc_multiome"
   }
 ]
diff --git a/results/predict_modality/data/method_info.json b/results/predict_modality/data/method_info.json
index f902f86f..683208dc 100644
--- a/results/predict_modality/data/method_info.json
+++ b/results/predict_modality/data/method_info.json
@@ -11,9 +11,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/control_methods/mean_per_gene:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/control_methods/mean_per_gene",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/control_methods/mean_per_gene",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "control_methods",
@@ -27,9 +27,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/control_methods/random_predict:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/control_methods/random_predict",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/control_methods/random_predict",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "control_methods",
@@ -43,9 +43,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/control_methods/zeros:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/control_methods/zeros",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/control_methods/zeros",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "control_methods",
@@ -59,9 +59,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/control_methods/solution:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/control_methods/solution",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/control_methods/solution",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "methods",
@@ -75,9 +75,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/methods/knnr_py:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/methods/knnr_py",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/methods/knnr_py",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "methods",
@@ -91,9 +91,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/methods/knnr_r:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/methods/knnr_r",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/methods/knnr_r",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "methods",
@@ -107,9 +107,9 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/methods/lm:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/methods/lm",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/methods/lm",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   },
   {
     "task_id": "methods",
@@ -123,8 +123,40 @@
     "code_url": "https://github.com/openproblems-bio/task_predict_modality",
     "documentation_url": null,
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/methods/guanlab_dengkw_pm:build_main",
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/methods/guanlab_dengkw_pm",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/methods/guanlab_dengkw_pm",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197"
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "novel",
+    "method_name": "Novel",
+    "method_summary": "A method using encoder-decoder MLP model",
+    "method_description": "This method trains an encoder-decoder MLP model with one output neuron per component in the target. As an input, the encoders use representations obtained from ATAC and GEX data via LSI transform and raw ADT data. The hyperparameters of the models were found via broad hyperparameter search using the Optuna framework.",
+    "is_baseline": false,
+    "references_doi": "10.1101/2022.04.11.487796",
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/neurips2021_multimodal_topmethods/tree/main/src/predict_modality/methods/novel",
+    "documentation_url": "https://github.com/openproblems-bio/neurips2021_multimodal_topmethods/tree/main/src/predict_modality/methods/novel#readme",
+    "image": "https://github.com/orgs/openproblems-bio/packages?repo_name=task_predict_modality&q=methods/novel/novel",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/methods/novel/novel",
+    "code_version": "build_main",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "simple_mlp",
+    "method_name": "Simple MLP",
+    "method_summary": "Ensemble of MLPs trained on different sites (team AXX)",
+    "method_description": "This folder contains the AXX solution to the OpenProblems-NeurIPS2021 Single-Cell Multimodal Data Integration.\nTeam took the 4th place of the modality prediction task in terms of overall ranking of 4 subtasks: namely GEX\nto ADT, ADT to GEX, GEX to ATAC and ATAC to GEX. Specifically, our methods ranked 3rd in GEX to ATAC and 4th\nin GEX to ADT. More details about the task can be found in the\n[competition webpage](https://openproblems.bio/events/2021-09_neurips/documentation/about_tasks/task1_modality_prediction).\n",
+    "is_baseline": false,
+    "references_doi": "10.1101/2022.04.11.487796",
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/neurips2021_multimodal_topmethods/tree/main/src/predict_modality/methods/AXX",
+    "documentation_url": "https://github.com/openproblems-bio/neurips2021_multimodal_topmethods/tree/main/src/predict_modality/methods/AXX",
+    "image": "https://github.com/orgs/openproblems-bio/packages?repo_name=task_predict_modality&q=methods/simple_mlp/simple_mlp",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/methods/simple_mlp/simple_mlp",
+    "code_version": "build_main",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1"
   }
 ]
diff --git a/results/predict_modality/data/metric_execution_info.json b/results/predict_modality/data/metric_execution_info.json
index 8e5c6f85..4fea0f69 100644
--- a/results/predict_modality/data/metric_execution_info.json
+++ b/results/predict_modality/data/metric_execution_info.json
@@ -4,11 +4,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:57:45",
+      "submit": "2025-01-09 16:00:40",
       "exit_code": 0,
-      "duration_sec": 34.2,
-      "cpu_pct": 164.2,
-      "peak_memory_mb": 1844,
+      "duration_sec": 36,
+      "cpu_pct": 211.6,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -18,12 +18,12 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:57:45",
+      "submit": "2025-01-09 16:00:39",
       "exit_code": 0,
-      "duration_sec": 4.2,
-      "cpu_pct": 212.7,
-      "peak_memory_mb": 772,
-      "disk_read_mb": 38,
+      "duration_sec": 3.8,
+      "cpu_pct": 475.2,
+      "peak_memory_mb": 2151,
+      "disk_read_mb": 40,
       "disk_write_mb": 2
     }
   },
@@ -32,11 +32,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:48:35",
+      "submit": "2025-01-09 15:40:49",
       "exit_code": 0,
-      "duration_sec": 32.4,
-      "cpu_pct": 285.3,
-      "peak_memory_mb": 3277,
+      "duration_sec": 63.6,
+      "cpu_pct": 132,
+      "peak_memory_mb": 3380,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -46,11 +46,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:48:35",
+      "submit": "2025-01-09 15:40:49",
       "exit_code": 0,
-      "duration_sec": 4.4,
-      "cpu_pct": 188,
-      "peak_memory_mb": 774,
+      "duration_sec": 12,
+      "cpu_pct": 138.6,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 40,
       "disk_write_mb": 2
     }
@@ -60,11 +60,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:48:35",
+      "submit": "2025-01-09 15:40:59",
       "exit_code": 0,
-      "duration_sec": 77.4,
-      "cpu_pct": 69.6,
-      "peak_memory_mb": 1844,
+      "duration_sec": 37.2,
+      "cpu_pct": 215.6,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -74,11 +74,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:48:35",
+      "submit": "2025-01-09 15:40:59",
       "exit_code": 0,
-      "duration_sec": 7.8,
-      "cpu_pct": 107.4,
-      "peak_memory_mb": 781,
+      "duration_sec": 11.6,
+      "cpu_pct": 141.6,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 40,
       "disk_write_mb": 2
     }
@@ -88,12 +88,12 @@
     "method_id": "lm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:49:55",
+      "submit": "2025-01-09 15:42:19",
       "exit_code": 0,
-      "duration_sec": 36.6,
-      "cpu_pct": 157.1,
-      "peak_memory_mb": 1844,
-      "disk_read_mb": 216,
+      "duration_sec": 50.4,
+      "cpu_pct": 177,
+      "peak_memory_mb": 3175,
+      "disk_read_mb": 222,
       "disk_write_mb": 6
     }
   },
@@ -102,11 +102,11 @@
     "method_id": "lm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:49:55",
+      "submit": "2025-01-09 15:42:19",
       "exit_code": 0,
-      "duration_sec": 14.6,
-      "cpu_pct": 63.6,
-      "peak_memory_mb": 1536,
+      "duration_sec": 11.2,
+      "cpu_pct": 151.3,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 40,
       "disk_write_mb": 2
     }
@@ -116,11 +116,11 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:45",
+      "submit": "2025-01-09 15:39:20",
       "exit_code": 0,
-      "duration_sec": 60,
-      "cpu_pct": 156,
-      "peak_memory_mb": 5940,
+      "duration_sec": 65.4,
+      "cpu_pct": 134.5,
+      "peak_memory_mb": 3277,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -130,25 +130,53 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:45",
+      "submit": "2025-01-09 15:39:20",
       "exit_code": 0,
-      "duration_sec": 3.6,
-      "cpu_pct": 416.1,
-      "peak_memory_mb": 1434,
+      "duration_sec": 9.8,
+      "cpu_pct": 149.8,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 38,
       "disk_write_mb": 2
     }
   },
   {
     "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
-    "method_id": "random_predict",
+    "method_id": "novel",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:25",
+      "submit": "2025-01-09 15:52:59",
       "exit_code": 0,
       "duration_sec": 33,
-      "cpu_pct": 168.1,
-      "peak_memory_mb": 1946,
+      "cpu_pct": 218.9,
+      "peak_memory_mb": 3277,
+      "disk_read_mb": 216,
+      "disk_write_mb": 6
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
+    "method_id": "novel",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2025-01-09 15:52:59",
+      "exit_code": 0,
+      "duration_sec": 3.8,
+      "cpu_pct": 323.1,
+      "peak_memory_mb": 1434,
+      "disk_read_mb": 40,
+      "disk_write_mb": 2
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
+    "method_id": "random_predict",
+    "metric_component_name": "correlation",
+    "resources": {
+      "submit": "2025-01-09 15:39:30",
+      "exit_code": 0,
+      "duration_sec": 79.2,
+      "cpu_pct": 96,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -158,12 +186,12 @@
     "method_id": "random_predict",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:25",
+      "submit": "2025-01-09 15:39:30",
       "exit_code": 0,
-      "duration_sec": 13.2,
-      "cpu_pct": 58.8,
-      "peak_memory_mb": 1536,
-      "disk_read_mb": 38,
+      "duration_sec": 11.4,
+      "cpu_pct": 141.6,
+      "peak_memory_mb": 2868,
+      "disk_read_mb": 40,
       "disk_write_mb": 2
     }
   },
@@ -172,11 +200,11 @@
     "method_id": "solution",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:25",
+      "submit": "2025-01-09 15:39:30",
       "exit_code": 0,
-      "duration_sec": 70.2,
-      "cpu_pct": 84.9,
-      "peak_memory_mb": 1844,
+      "duration_sec": 78,
+      "cpu_pct": 111.9,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -186,11 +214,11 @@
     "method_id": "solution",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:25",
+      "submit": "2025-01-09 15:39:30",
       "exit_code": 0,
-      "duration_sec": 4,
-      "cpu_pct": 162.3,
-      "peak_memory_mb": 772,
+      "duration_sec": 7.8,
+      "cpu_pct": 189.8,
+      "peak_memory_mb": 1434,
       "disk_read_mb": 40,
       "disk_write_mb": 2
     }
@@ -200,12 +228,12 @@
     "method_id": "zeros",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:35",
+      "submit": "2025-01-09 15:39:20",
       "exit_code": 0,
-      "duration_sec": 54,
-      "cpu_pct": 108.5,
-      "peak_memory_mb": 1844,
-      "disk_read_mb": 210,
+      "duration_sec": 71.4,
+      "cpu_pct": 128.1,
+      "peak_memory_mb": 3175,
+      "disk_read_mb": 216,
       "disk_write_mb": 6
     }
   },
@@ -214,11 +242,11 @@
     "method_id": "zeros",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:35",
+      "submit": "2025-01-09 15:39:20",
       "exit_code": 0,
-      "duration_sec": 13.4,
-      "cpu_pct": 64.6,
-      "peak_memory_mb": 1536,
+      "duration_sec": 3.8,
+      "cpu_pct": 398.8,
+      "peak_memory_mb": 1434,
       "disk_read_mb": 38,
       "disk_write_mb": 2
     }
@@ -228,11 +256,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:49:55",
+      "submit": "2025-01-09 15:50:29",
       "exit_code": 0,
-      "duration_sec": 281.4,
-      "cpu_pct": 100.7,
-      "peak_memory_mb": 4506,
+      "duration_sec": 261.6,
+      "cpu_pct": 115.6,
+      "peak_memory_mb": 5837,
       "disk_read_mb": 588,
       "disk_write_mb": 6
     }
@@ -242,12 +270,12 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:49:55",
+      "submit": "2025-01-09 15:50:29",
       "exit_code": 0,
-      "duration_sec": 12.8,
-      "cpu_pct": 204,
-      "peak_memory_mb": 6452,
-      "disk_read_mb": 162,
+      "duration_sec": 6.4,
+      "cpu_pct": 239.9,
+      "peak_memory_mb": 2970,
+      "disk_read_mb": 164,
       "disk_write_mb": 2
     }
   },
@@ -256,11 +284,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:46:35",
+      "submit": "2025-01-09 15:37:49",
       "exit_code": 0,
-      "duration_sec": 243.6,
-      "cpu_pct": 107.1,
-      "peak_memory_mb": 8500,
+      "duration_sec": 274.8,
+      "cpu_pct": 98.1,
+      "peak_memory_mb": 4506,
       "disk_read_mb": 630,
       "disk_write_mb": 6
     }
@@ -270,11 +298,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:46:35",
+      "submit": "2025-01-09 15:37:49",
       "exit_code": 0,
-      "duration_sec": 30.2,
-      "cpu_pct": 90.3,
-      "peak_memory_mb": 2765,
+      "duration_sec": 6.4,
+      "cpu_pct": 275.6,
+      "peak_memory_mb": 2970,
       "disk_read_mb": 178,
       "disk_write_mb": 2
     }
@@ -284,10 +312,10 @@
     "method_id": "knnr_r",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:45",
+      "submit": "2025-01-09 15:40:59",
       "exit_code": 0,
-      "duration_sec": 211.2,
-      "cpu_pct": 125.3,
+      "duration_sec": 214.8,
+      "cpu_pct": 122.2,
       "peak_memory_mb": 5735,
       "disk_read_mb": 534,
       "disk_write_mb": 6
@@ -298,11 +326,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:45",
+      "submit": "2025-01-09 15:40:59",
       "exit_code": 0,
-      "duration_sec": 14.8,
-      "cpu_pct": 132.8,
-      "peak_memory_mb": 5530,
+      "duration_sec": 13,
+      "cpu_pct": 142.7,
+      "peak_memory_mb": 2970,
       "disk_read_mb": 146,
       "disk_write_mb": 2
     }
@@ -312,13 +340,13 @@
     "method_id": "lm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 14:55:25",
-      "exit_code": 0,
-      "duration_sec": 201.6,
-      "cpu_pct": 104.1,
-      "peak_memory_mb": 5940,
-      "disk_read_mb": 912,
-      "disk_write_mb": 6
+      "submit": "2025-01-09 17:34:55",
+      "exit_code": "NA",
+      "duration_sec": 60126,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
     }
   },
   {
@@ -326,11 +354,11 @@
     "method_id": "lm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 14:55:25",
+      "submit": "2025-01-09 17:34:55",
       "exit_code": 0,
-      "duration_sec": 4.8,
-      "cpu_pct": 286.7,
-      "peak_memory_mb": 3380,
+      "duration_sec": 16,
+      "cpu_pct": 121.8,
+      "peak_memory_mb": 3482,
       "disk_read_mb": 270,
       "disk_write_mb": 2
     }
@@ -340,10 +368,10 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:46:15",
+      "submit": "2025-01-09 15:39:29",
       "exit_code": 0,
-      "duration_sec": 162.6,
-      "cpu_pct": 129.8,
+      "duration_sec": 200.4,
+      "cpu_pct": 110.8,
       "peak_memory_mb": 5940,
       "disk_read_mb": 306,
       "disk_write_mb": 6
@@ -354,24 +382,52 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:46:15",
+      "submit": "2025-01-09 15:39:29",
       "exit_code": 0,
-      "duration_sec": 6.2,
-      "cpu_pct": 179.9,
-      "peak_memory_mb": 1536,
+      "duration_sec": 17.6,
+      "cpu_pct": 94.8,
+      "peak_memory_mb": 3892,
       "disk_read_mb": 68,
       "disk_write_mb": 2
     }
   },
+  {
+    "dataset_id": "openproblems_neurips2021/bmmc_cite/swap",
+    "method_id": "novel",
+    "metric_component_name": "correlation",
+    "resources": {
+      "submit": "2025-01-09 16:10:19",
+      "exit_code": 0,
+      "duration_sec": 258.6,
+      "cpu_pct": 118.3,
+      "peak_memory_mb": 5837,
+      "disk_read_mb": 582,
+      "disk_write_mb": 6
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2021/bmmc_cite/swap",
+    "method_id": "novel",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2025-01-09 16:10:19",
+      "exit_code": 0,
+      "duration_sec": 6.4,
+      "cpu_pct": 260.8,
+      "peak_memory_mb": 3789,
+      "disk_read_mb": 160,
+      "disk_write_mb": 2
+    }
+  },
   {
     "dataset_id": "openproblems_neurips2021/bmmc_cite/swap",
     "method_id": "random_predict",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:45:55",
+      "submit": "2025-01-09 15:39:30",
       "exit_code": 0,
-      "duration_sec": 106.2,
-      "cpu_pct": 124.8,
+      "duration_sec": 195.6,
+      "cpu_pct": 106.6,
       "peak_memory_mb": 5530,
       "disk_read_mb": 324,
       "disk_write_mb": 6
@@ -382,12 +438,12 @@
     "method_id": "random_predict",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:45:55",
+      "submit": "2025-01-09 15:39:29",
       "exit_code": 0,
-      "duration_sec": 17.6,
-      "cpu_pct": 71.7,
-      "peak_memory_mb": 2868,
-      "disk_read_mb": 74,
+      "duration_sec": 15,
+      "cpu_pct": 111.2,
+      "peak_memory_mb": 3072,
+      "disk_read_mb": 76,
       "disk_write_mb": 2
     }
   },
@@ -396,12 +452,12 @@
     "method_id": "solution",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:45:45",
+      "submit": "2025-01-09 15:36:00",
       "exit_code": 0,
-      "duration_sec": 99,
-      "cpu_pct": 152.6,
-      "peak_memory_mb": 8192,
-      "disk_read_mb": 360,
+      "duration_sec": 150.6,
+      "cpu_pct": 114.7,
+      "peak_memory_mb": 4199,
+      "disk_read_mb": 366,
       "disk_write_mb": 6
     }
   },
@@ -410,11 +466,11 @@
     "method_id": "solution",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:45:45",
+      "submit": "2025-01-09 15:36:00",
       "exit_code": 0,
-      "duration_sec": 18,
-      "cpu_pct": 75.9,
-      "peak_memory_mb": 2868,
+      "duration_sec": 5.2,
+      "cpu_pct": 201.2,
+      "peak_memory_mb": 1536,
       "disk_read_mb": 88,
       "disk_write_mb": 2
     }
@@ -424,10 +480,10 @@
     "method_id": "zeros",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:45:55",
+      "submit": "2025-01-09 15:39:19",
       "exit_code": 0,
-      "duration_sec": 70.2,
-      "cpu_pct": 161.1,
+      "duration_sec": 113.4,
+      "cpu_pct": 140.7,
       "peak_memory_mb": 5428,
       "disk_read_mb": 288,
       "disk_write_mb": 6
@@ -438,11 +494,11 @@
     "method_id": "zeros",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:45:55",
+      "submit": "2025-01-09 15:39:20",
       "exit_code": 0,
-      "duration_sec": 4,
-      "cpu_pct": 329,
-      "peak_memory_mb": 1434,
+      "duration_sec": 15.6,
+      "cpu_pct": 106,
+      "peak_memory_mb": 2970,
       "disk_read_mb": 64,
       "disk_write_mb": 2
     }
@@ -452,11 +508,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 19:36:45",
+      "submit": "2025-01-09 16:23:09",
       "exit_code": 0,
-      "duration_sec": 187.2,
-      "cpu_pct": 144.5,
-      "peak_memory_mb": 7373,
+      "duration_sec": 189.6,
+      "cpu_pct": 134.3,
+      "peak_memory_mb": 5940,
       "disk_read_mb": 606,
       "disk_write_mb": 6
     }
@@ -466,11 +522,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 19:36:45",
+      "submit": "2025-01-09 16:23:09",
       "exit_code": 0,
-      "duration_sec": 6.6,
-      "cpu_pct": 307.5,
-      "peak_memory_mb": 6247,
+      "duration_sec": 6.2,
+      "cpu_pct": 299.1,
+      "peak_memory_mb": 4301,
       "disk_read_mb": 170,
       "disk_write_mb": 2
     }
@@ -480,11 +536,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:05",
+      "submit": "2025-01-09 15:40:19",
       "exit_code": 0,
-      "duration_sec": 148.8,
-      "cpu_pct": 113.6,
-      "peak_memory_mb": 3277,
+      "duration_sec": 183,
+      "cpu_pct": 123.4,
+      "peak_memory_mb": 5940,
       "disk_read_mb": 576,
       "disk_write_mb": 6
     }
@@ -494,11 +550,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:05",
+      "submit": "2025-01-09 15:40:19",
       "exit_code": 0,
-      "duration_sec": 29.2,
-      "cpu_pct": 92.8,
-      "peak_memory_mb": 2765,
+      "duration_sec": 6.4,
+      "cpu_pct": 251.4,
+      "peak_memory_mb": 3277,
       "disk_read_mb": 160,
       "disk_write_mb": 2
     }
@@ -508,12 +564,12 @@
     "method_id": "knnr_r",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:05",
+      "submit": "2025-01-09 15:40:09",
       "exit_code": 0,
-      "duration_sec": 118.2,
-      "cpu_pct": 111.2,
-      "peak_memory_mb": 3175,
-      "disk_read_mb": 486,
+      "duration_sec": 117,
+      "cpu_pct": 142.4,
+      "peak_memory_mb": 4506,
+      "disk_read_mb": 492,
       "disk_write_mb": 6
     }
   },
@@ -522,11 +578,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:05",
+      "submit": "2025-01-09 15:40:09",
       "exit_code": 0,
-      "duration_sec": 6,
-      "cpu_pct": 224.9,
-      "peak_memory_mb": 2868,
+      "duration_sec": 15,
+      "cpu_pct": 159.1,
+      "peak_memory_mb": 4404,
       "disk_read_mb": 130,
       "disk_write_mb": 2
     }
@@ -536,11 +592,11 @@
     "method_id": "lm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 14:05:05",
+      "submit": "2025-01-09 16:10:29",
       "exit_code": 0,
-      "duration_sec": 186.6,
-      "cpu_pct": 111,
-      "peak_memory_mb": 3380,
+      "duration_sec": 188.4,
+      "cpu_pct": 124.2,
+      "peak_memory_mb": 4711,
       "disk_read_mb": 834,
       "disk_write_mb": 6
     }
@@ -550,11 +606,11 @@
     "method_id": "lm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 14:05:05",
+      "submit": "2025-01-09 16:10:29",
       "exit_code": 0,
-      "duration_sec": 6.6,
-      "cpu_pct": 171.1,
-      "peak_memory_mb": 2253,
+      "duration_sec": 7.2,
+      "cpu_pct": 233.2,
+      "peak_memory_mb": 3277,
       "disk_read_mb": 246,
       "disk_write_mb": 2
     }
@@ -564,11 +620,11 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:46:35",
+      "submit": "2025-01-09 15:39:29",
       "exit_code": 0,
-      "duration_sec": 163.2,
-      "cpu_pct": 108.9,
-      "peak_memory_mb": 7476,
+      "duration_sec": 155.4,
+      "cpu_pct": 115.1,
+      "peak_memory_mb": 4711,
       "disk_read_mb": 402,
       "disk_write_mb": 6
     }
@@ -578,11 +634,11 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:46:35",
+      "submit": "2025-01-09 15:39:29",
       "exit_code": 0,
-      "duration_sec": 29.4,
-      "cpu_pct": 82.4,
-      "peak_memory_mb": 2765,
+      "duration_sec": 16.2,
+      "cpu_pct": 112.4,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 102,
       "disk_write_mb": 2
     }
@@ -592,11 +648,11 @@
     "method_id": "random_predict",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:46:15",
+      "submit": "2025-01-09 15:39:40",
       "exit_code": 0,
-      "duration_sec": 86.4,
-      "cpu_pct": 158.7,
-      "peak_memory_mb": 4404,
+      "duration_sec": 87,
+      "cpu_pct": 174.4,
+      "peak_memory_mb": 7168,
       "disk_read_mb": 402,
       "disk_write_mb": 6
     }
@@ -606,11 +662,11 @@
     "method_id": "random_predict",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:46:15",
+      "submit": "2025-01-09 15:39:39",
       "exit_code": 0,
-      "duration_sec": 5.6,
-      "cpu_pct": 197.1,
-      "peak_memory_mb": 2868,
+      "duration_sec": 15.6,
+      "cpu_pct": 135.9,
+      "peak_memory_mb": 4301,
       "disk_read_mb": 102,
       "disk_write_mb": 2
     }
@@ -620,11 +676,11 @@
     "method_id": "solution",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:47:25",
+      "submit": "2025-01-09 15:39:30",
       "exit_code": 0,
-      "duration_sec": 96.6,
-      "cpu_pct": 111.2,
-      "peak_memory_mb": 3175,
+      "duration_sec": 137.4,
+      "cpu_pct": 101.2,
+      "peak_memory_mb": 4506,
       "disk_read_mb": 564,
       "disk_write_mb": 6
     }
@@ -634,12 +690,12 @@
     "method_id": "solution",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:47:25",
+      "submit": "2025-01-09 15:39:30",
       "exit_code": 0,
-      "duration_sec": 17.2,
-      "cpu_pct": 62.6,
-      "peak_memory_mb": 1536,
-      "disk_read_mb": 154,
+      "duration_sec": 16.2,
+      "cpu_pct": 106.8,
+      "peak_memory_mb": 2970,
+      "disk_read_mb": 156,
       "disk_write_mb": 2
     }
   },
@@ -648,11 +704,11 @@
     "method_id": "zeros",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:46:35",
+      "submit": "2025-01-09 15:39:40",
       "exit_code": 0,
-      "duration_sec": 135,
-      "cpu_pct": 107.9,
-      "peak_memory_mb": 7168,
+      "duration_sec": 108.6,
+      "cpu_pct": 141.8,
+      "peak_memory_mb": 5735,
       "disk_read_mb": 390,
       "disk_write_mb": 6
     }
@@ -662,12 +718,12 @@
     "method_id": "zeros",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:46:35",
+      "submit": "2025-01-09 15:39:39",
       "exit_code": 0,
-      "duration_sec": 28.2,
-      "cpu_pct": 76.1,
-      "peak_memory_mb": 2765,
-      "disk_read_mb": 96,
+      "duration_sec": 9,
+      "cpu_pct": 163.8,
+      "peak_memory_mb": 2970,
+      "disk_read_mb": 98,
       "disk_write_mb": 2
     }
   },
@@ -676,11 +732,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:55:05",
+      "submit": "2025-01-09 16:00:39",
       "exit_code": 0,
-      "duration_sec": 240.6,
-      "cpu_pct": 109,
-      "peak_memory_mb": 3892,
+      "duration_sec": 249,
+      "cpu_pct": 117.7,
+      "peak_memory_mb": 5223,
       "disk_read_mb": 546,
       "disk_write_mb": 6
     }
@@ -690,11 +746,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:55:05",
+      "submit": "2025-01-09 16:00:39",
       "exit_code": 0,
-      "duration_sec": 5.8,
-      "cpu_pct": 180.7,
-      "peak_memory_mb": 1639,
+      "duration_sec": 6.4,
+      "cpu_pct": 250.4,
+      "peak_memory_mb": 2970,
       "disk_read_mb": 148,
       "disk_write_mb": 2
     }
@@ -704,11 +760,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:53:25",
+      "submit": "2025-01-09 15:44:19",
       "exit_code": 0,
-      "duration_sec": 248.4,
-      "cpu_pct": 104.5,
-      "peak_memory_mb": 4199,
+      "duration_sec": 279,
+      "cpu_pct": 115,
+      "peak_memory_mb": 5530,
       "disk_read_mb": 606,
       "disk_write_mb": 6
     }
@@ -718,11 +774,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:53:25",
+      "submit": "2025-01-09 15:44:19",
       "exit_code": 0,
-      "duration_sec": 17,
-      "cpu_pct": 62.2,
-      "peak_memory_mb": 2356,
+      "duration_sec": 8.2,
+      "cpu_pct": 179.4,
+      "peak_memory_mb": 2970,
       "disk_read_mb": 168,
       "disk_write_mb": 2
     }
@@ -732,11 +788,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:51:35",
+      "submit": "2025-01-09 15:45:00",
       "exit_code": 0,
-      "duration_sec": 207,
-      "cpu_pct": 110.4,
-      "peak_memory_mb": 4199,
+      "duration_sec": 240.6,
+      "cpu_pct": 114.5,
+      "peak_memory_mb": 5530,
       "disk_read_mb": 510,
       "disk_write_mb": 6
     }
@@ -746,10 +802,10 @@
     "method_id": "knnr_r",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:51:35",
+      "submit": "2025-01-09 15:44:59",
       "exit_code": 0,
-      "duration_sec": 4.6,
-      "cpu_pct": 291.7,
+      "duration_sec": 7.2,
+      "cpu_pct": 218.2,
       "peak_memory_mb": 3482,
       "disk_read_mb": 138,
       "disk_write_mb": 2
@@ -760,11 +816,11 @@
     "method_id": "lm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 14:17:45",
+      "submit": "2025-01-09 16:25:09",
       "exit_code": 0,
-      "duration_sec": 255.6,
-      "cpu_pct": 108.5,
-      "peak_memory_mb": 3994,
+      "duration_sec": 242.4,
+      "cpu_pct": 127.9,
+      "peak_memory_mb": 6656,
       "disk_read_mb": 852,
       "disk_write_mb": 6
     }
@@ -774,12 +830,12 @@
     "method_id": "lm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 14:17:45",
+      "submit": "2025-01-09 16:25:09",
       "exit_code": 0,
-      "duration_sec": 7,
-      "cpu_pct": 166.3,
-      "peak_memory_mb": 2458,
-      "disk_read_mb": 250,
+      "duration_sec": 6.8,
+      "cpu_pct": 328.1,
+      "peak_memory_mb": 5120,
+      "disk_read_mb": 252,
       "disk_write_mb": 2
     }
   },
@@ -788,11 +844,11 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:49:15",
+      "submit": "2025-01-09 15:43:39",
       "exit_code": 0,
-      "duration_sec": 196.8,
-      "cpu_pct": 94.4,
-      "peak_memory_mb": 3994,
+      "duration_sec": 153,
+      "cpu_pct": 129.5,
+      "peak_memory_mb": 5325,
       "disk_read_mb": 270,
       "disk_write_mb": 6
     }
@@ -802,11 +858,11 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:49:15",
+      "submit": "2025-01-09 15:43:39",
       "exit_code": 0,
-      "duration_sec": 12.6,
-      "cpu_pct": 89.2,
-      "peak_memory_mb": 1536,
+      "duration_sec": 5.6,
+      "cpu_pct": 371.5,
+      "peak_memory_mb": 4506,
       "disk_read_mb": 58,
       "disk_write_mb": 2
     }
@@ -816,12 +872,12 @@
     "method_id": "random_predict",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:50:15",
+      "submit": "2025-01-09 15:43:59",
       "exit_code": 0,
-      "duration_sec": 120.6,
-      "cpu_pct": 110.4,
+      "duration_sec": 132.6,
+      "cpu_pct": 132.2,
       "peak_memory_mb": 5325,
-      "disk_read_mb": 288,
+      "disk_read_mb": 294,
       "disk_write_mb": 6
     }
   },
@@ -830,11 +886,11 @@
     "method_id": "random_predict",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:50:15",
+      "submit": "2025-01-09 15:43:59",
       "exit_code": 0,
-      "duration_sec": 15.4,
-      "cpu_pct": 65.4,
-      "peak_memory_mb": 1639,
+      "duration_sec": 5.8,
+      "cpu_pct": 283.4,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 64,
       "disk_write_mb": 2
     }
@@ -844,11 +900,11 @@
     "method_id": "solution",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:48:15",
+      "submit": "2025-01-09 15:39:49",
       "exit_code": 0,
-      "duration_sec": 172.8,
-      "cpu_pct": 88.3,
-      "peak_memory_mb": 3994,
+      "duration_sec": 168,
+      "cpu_pct": 114.9,
+      "peak_memory_mb": 5325,
       "disk_read_mb": 300,
       "disk_write_mb": 6
     }
@@ -858,12 +914,12 @@
     "method_id": "solution",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:48:15",
+      "submit": "2025-01-09 15:39:49",
       "exit_code": 0,
-      "duration_sec": 12.2,
-      "cpu_pct": 76.7,
-      "peak_memory_mb": 1536,
-      "disk_read_mb": 66,
+      "duration_sec": 13.4,
+      "cpu_pct": 108.2,
+      "peak_memory_mb": 2868,
+      "disk_read_mb": 68,
       "disk_write_mb": 2
     }
   },
@@ -872,11 +928,11 @@
     "method_id": "zeros",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:50:15",
+      "submit": "2025-01-09 15:40:49",
       "exit_code": 0,
-      "duration_sec": 94.8,
-      "cpu_pct": 124.3,
-      "peak_memory_mb": 3892,
+      "duration_sec": 128.4,
+      "cpu_pct": 117.4,
+      "peak_memory_mb": 5223,
       "disk_read_mb": 258,
       "disk_write_mb": 6
     }
@@ -886,12 +942,12 @@
     "method_id": "zeros",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:50:15",
+      "submit": "2025-01-09 15:40:49",
       "exit_code": 0,
-      "duration_sec": 3.2,
-      "cpu_pct": 409,
-      "peak_memory_mb": 1434,
-      "disk_read_mb": 52,
+      "duration_sec": 5.2,
+      "cpu_pct": 274.6,
+      "peak_memory_mb": 2868,
+      "disk_read_mb": 54,
       "disk_write_mb": 2
     }
   },
@@ -900,11 +956,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 14:34:05",
+      "submit": "2025-01-09 16:48:49",
       "exit_code": 0,
-      "duration_sec": 22.2,
-      "cpu_pct": 428.6,
-      "peak_memory_mb": 5940,
+      "duration_sec": 33.6,
+      "cpu_pct": 320.1,
+      "peak_memory_mb": 4608,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -914,12 +970,12 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 14:34:05",
+      "submit": "2025-01-09 16:48:49",
       "exit_code": 0,
-      "duration_sec": 3,
-      "cpu_pct": 638,
-      "peak_memory_mb": 2765,
-      "disk_read_mb": 38,
+      "duration_sec": 5.2,
+      "cpu_pct": 345.6,
+      "peak_memory_mb": 4199,
+      "disk_read_mb": 40,
       "disk_write_mb": 2
     }
   },
@@ -928,11 +984,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 14:00:55",
+      "submit": "2025-01-09 15:50:09",
       "exit_code": 0,
-      "duration_sec": 45,
-      "cpu_pct": 133.3,
-      "peak_memory_mb": 1946,
+      "duration_sec": 34.2,
+      "cpu_pct": 243.7,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -942,11 +998,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 14:00:55",
+      "submit": "2025-01-09 15:50:09",
       "exit_code": 0,
-      "duration_sec": 7.4,
-      "cpu_pct": 122.5,
-      "peak_memory_mb": 1536,
+      "duration_sec": 4,
+      "cpu_pct": 294.5,
+      "peak_memory_mb": 1434,
       "disk_read_mb": 40,
       "disk_write_mb": 2
     }
@@ -956,11 +1012,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:58:15",
+      "submit": "2025-01-09 15:47:39",
       "exit_code": 0,
-      "duration_sec": 33,
-      "cpu_pct": 170.9,
-      "peak_memory_mb": 1946,
+      "duration_sec": 34.2,
+      "cpu_pct": 249.1,
+      "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -970,12 +1026,12 @@
     "method_id": "knnr_r",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:58:15",
+      "submit": "2025-01-09 15:47:39",
       "exit_code": 0,
-      "duration_sec": 3.8,
-      "cpu_pct": 212.9,
-      "peak_memory_mb": 770,
-      "disk_read_mb": 38,
+      "duration_sec": 4.4,
+      "cpu_pct": 217.4,
+      "peak_memory_mb": 2868,
+      "disk_read_mb": 40,
       "disk_write_mb": 2
     }
   },
@@ -984,12 +1040,12 @@
     "method_id": "lm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 14:09:35",
+      "submit": "2025-01-09 15:58:29",
       "exit_code": 0,
       "duration_sec": 34.8,
-      "cpu_pct": 157.1,
-      "peak_memory_mb": 1844,
-      "disk_read_mb": 216,
+      "cpu_pct": 220,
+      "peak_memory_mb": 3175,
+      "disk_read_mb": 222,
       "disk_write_mb": 6
     }
   },
@@ -998,11 +1054,11 @@
     "method_id": "lm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 14:09:35",
+      "submit": "2025-01-09 15:58:29",
       "exit_code": 0,
       "duration_sec": 4,
-      "cpu_pct": 218.7,
-      "peak_memory_mb": 772,
+      "cpu_pct": 338.3,
+      "peak_memory_mb": 1434,
       "disk_read_mb": 40,
       "disk_write_mb": 2
     }
@@ -1012,12 +1068,12 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:52:05",
+      "submit": "2025-01-09 15:44:19",
       "exit_code": 0,
-      "duration_sec": 32.4,
-      "cpu_pct": 159.7,
-      "peak_memory_mb": 1946,
-      "disk_read_mb": 210,
+      "duration_sec": 36.6,
+      "cpu_pct": 168,
+      "peak_memory_mb": 3175,
+      "disk_read_mb": 216,
       "disk_write_mb": 6
     }
   },
@@ -1026,11 +1082,11 @@
     "method_id": "mean_per_gene",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:52:05",
+      "submit": "2025-01-09 15:44:19",
       "exit_code": 0,
-      "duration_sec": 3.8,
-      "cpu_pct": 221.8,
-      "peak_memory_mb": 773,
+      "duration_sec": 5.6,
+      "cpu_pct": 189.6,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 38,
       "disk_write_mb": 2
     }
@@ -1040,11 +1096,11 @@
     "method_id": "random_predict",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:52:05",
+      "submit": "2025-01-09 15:43:40",
       "exit_code": 0,
-      "duration_sec": 22.2,
-      "cpu_pct": 415.4,
-      "peak_memory_mb": 5940,
+      "duration_sec": 33.6,
+      "cpu_pct": 274.5,
+      "peak_memory_mb": 4608,
       "disk_read_mb": 216,
       "disk_write_mb": 6
     }
@@ -1054,11 +1110,11 @@
     "method_id": "random_predict",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:52:05",
+      "submit": "2025-01-09 15:43:40",
       "exit_code": 0,
-      "duration_sec": 4,
-      "cpu_pct": 215.5,
-      "peak_memory_mb": 770,
+      "duration_sec": 5.4,
+      "cpu_pct": 292,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 38,
       "disk_write_mb": 2
     }
@@ -1068,10 +1124,10 @@
     "method_id": "solution",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:52:05",
+      "submit": "2025-01-09 15:43:49",
       "exit_code": 0,
-      "duration_sec": 22.8,
-      "cpu_pct": 307.7,
+      "duration_sec": 34.8,
+      "cpu_pct": 233.4,
       "peak_memory_mb": 3175,
       "disk_read_mb": 216,
       "disk_write_mb": 6
@@ -1082,12 +1138,12 @@
     "method_id": "solution",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:52:05",
+      "submit": "2025-01-09 15:43:49",
       "exit_code": 0,
-      "duration_sec": 2.8,
-      "cpu_pct": 414.6,
-      "peak_memory_mb": 1434,
-      "disk_read_mb": 38,
+      "duration_sec": 5.2,
+      "cpu_pct": 292.2,
+      "peak_memory_mb": 2868,
+      "disk_read_mb": 40,
       "disk_write_mb": 2
     }
   },
@@ -1096,12 +1152,12 @@
     "method_id": "zeros",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:51:45",
+      "submit": "2025-01-09 15:43:10",
       "exit_code": 0,
       "duration_sec": 33,
-      "cpu_pct": 169.7,
-      "peak_memory_mb": 1844,
-      "disk_read_mb": 210,
+      "cpu_pct": 221.8,
+      "peak_memory_mb": 3277,
+      "disk_read_mb": 216,
       "disk_write_mb": 6
     }
   },
@@ -1110,11 +1166,11 @@
     "method_id": "zeros",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:51:45",
+      "submit": "2025-01-09 15:43:09",
       "exit_code": 0,
-      "duration_sec": 3.8,
-      "cpu_pct": 222.9,
-      "peak_memory_mb": 774,
+      "duration_sec": 11.6,
+      "cpu_pct": 147.2,
+      "peak_memory_mb": 2868,
       "disk_read_mb": 38,
       "disk_write_mb": 2
     }
@@ -1124,11 +1180,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 19:36:25",
+      "submit": "2025-01-09 16:07:39",
       "exit_code": 0,
-      "duration_sec": 438,
-      "cpu_pct": 119.6,
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@@ -1138,11 +1194,11 @@
     "method_id": "guanlab_dengkw_pm",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 19:36:25",
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       "duration_sec": 8.2,
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@@ -1152,11 +1208,11 @@
     "method_id": "knnr_py",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:53:25",
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@@ -1166,12 +1222,12 @@
     "method_id": "knnr_py",
     "metric_component_name": "mse",
     "resources": {
-      "submit": "2024-11-25 13:53:25",
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@@ -1180,11 +1236,11 @@
     "method_id": "knnr_r",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 13:58:35",
+      "submit": "2025-01-09 15:45:19",
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       "disk_read_mb": 768,
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@@ -1194,152 +1250,516 @@
     "method_id": "knnr_r",
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     "resources": {
-      "submit": "2024-11-25 13:58:35",
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-      "cpu_pct": 152.1,
-      "peak_memory_mb": 1946,
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     "dataset_id": "openproblems_neurips2022/pbmc_cite/swap",
-    "method_id": "lm",
+    "method_id": "mean_per_gene",
     "metric_component_name": "correlation",
     "resources": {
-      "submit": "2024-11-25 18:51:15",
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       "exit_code": 0,
-      "duration_sec": 6,
-      "cpu_pct": 160.9,
-      "peak_memory_mb": 1536,
-      "disk_read_mb": 100,
+      "duration_sec": 4.6,
+      "cpu_pct": 320,
+      "peak_memory_mb": 3072,
+      "disk_read_mb": 74,
       "disk_write_mb": 2
     }
   }
diff --git a/results/predict_modality/data/metric_info.json b/results/predict_modality/data/metric_info.json
index bf6c8407..69aa3a66 100644
--- a/results/predict_modality/data/metric_info.json
+++ b/results/predict_modality/data/metric_info.json
@@ -8,10 +8,10 @@
     "metric_description": "The mean of the pearson values of per-cell expression value vectors.",
     "references_doi": "10.1098/rspl.1895.0041",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/correlation",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/correlation",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/correlation:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": true
   },
   {
@@ -23,10 +23,10 @@
     "metric_description": "The mean of the spearman values of per-cell expression value vectors.",
     "references_doi": "10.1093/biomet/30.1-2.81",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/correlation",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/correlation",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/correlation:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": true
   },
   {
@@ -38,10 +38,10 @@
     "metric_description": "The mean of the pearson values of per-gene expression value vectors.",
     "references_doi": "10.1098/rspl.1895.0041",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/correlation",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/correlation",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/correlation:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": true
   },
   {
@@ -53,10 +53,10 @@
     "metric_description": "The mean of the spearman values of per-gene expression value vectors.",
     "references_doi": "10.1093/biomet/30.1-2.81",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/correlation",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/correlation",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/correlation:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": true
   },
   {
@@ -68,10 +68,10 @@
     "metric_description": "The mean of the pearson values of vectorized expression matrices.",
     "references_doi": "10.1098/rspl.1895.0041",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/correlation",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/correlation",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/correlation:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": true
   },
   {
@@ -83,10 +83,10 @@
     "metric_description": "The mean of the spearman values of vectorized expression matrices.",
     "references_doi": "10.1093/biomet/30.1-2.81",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/correlation",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/correlation",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/correlation:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": true
   },
   {
@@ -98,10 +98,10 @@
     "metric_description": "The square root of the mean of the square of all of the error.",
     "references_doi": "10.5194/gmdd-7-1525-2014",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/mse",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/mse",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/mse:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": false
   },
   {
@@ -113,10 +113,10 @@
     "metric_description": "The average difference between the expression values and the predicted expression values.",
     "references_doi": "10.5194/gmdd-7-1525-2014",
     "references_bibtex": null,
-    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/0bd597e201b39fbcbc1fcd7047f7654a9713a197/src/metrics/mse",
+    "implementation_url": "https://github.com/openproblems-bio/task_predict_modality/blob/b333268bf19de5c7b9003f69a864bda48ae827a1/src/metrics/mse",
     "image": "https://ghcr.io/openproblems-bio/task_predict_modality/metrics/mse:build_main",
     "code_version": "build_main",
-    "commit_sha": "0bd597e201b39fbcbc1fcd7047f7654a9713a197",
+    "commit_sha": "b333268bf19de5c7b9003f69a864bda48ae827a1",
     "maximize": false
   }
 ]
diff --git a/results/predict_modality/data/quality_control.json b/results/predict_modality/data/quality_control.json
index 5c238a19..ada5fabb 100644
--- a/results/predict_modality/data/quality_control.json
+++ b/results/predict_modality/data/quality_control.json
@@ -93,7 +93,7 @@
         "task_id": "task_predict_modality", 
         "category": "Method info", 
         "name": "Pct 'paper_reference' missing", 
-        "value": 0.5555555555555556, 
+        "value": 0.6, 
         "severity": 2, 
         "severity_value": 3.0, 
         "code": "percent_missing(method_info, field)", 
@@ -243,91 +243,91 @@
         "task_id": "task_predict_modality", 
         "category": "Raw data", 
         "name": "Number of results", 
-        "value": 72, 
+        "value": 80, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", 
-        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: task_predict_modality\n  Number of results: 72\n  Number of methods: 9\n  Number of metrics: 8\n  Number of datasets: 8\n"
+        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: task_predict_modality\n  Number of results: 80\n  Number of methods: 10\n  Number of metrics: 8\n  Number of datasets: 8\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'mean_pearson_per_cell' %missing", 
-        "value": 0.05555555555555558, 
-        "severity": 0, 
-        "severity_value": 0.5555555555555558, 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_pearson_per_cell\n  Percentage missing: 6%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_pearson_per_cell\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'mean_spearman_per_cell' %missing", 
-        "value": 0.05555555555555558, 
-        "severity": 0, 
-        "severity_value": 0.5555555555555558, 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_spearman_per_cell\n  Percentage missing: 6%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_spearman_per_cell\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'mean_pearson_per_gene' %missing", 
-        "value": 0.05555555555555558, 
-        "severity": 0, 
-        "severity_value": 0.5555555555555558, 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_pearson_per_gene\n  Percentage missing: 6%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_pearson_per_gene\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'mean_spearman_per_gene' %missing", 
-        "value": 0.05555555555555558, 
-        "severity": 0, 
-        "severity_value": 0.5555555555555558, 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_spearman_per_gene\n  Percentage missing: 6%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mean_spearman_per_gene\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'overall_pearson' %missing", 
-        "value": 0.16666666666666663, 
-        "severity": 1, 
-        "severity_value": 1.6666666666666663, 
+        "value": 0.32499999999999996, 
+        "severity": 3, 
+        "severity_value": 3.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: overall_pearson\n  Percentage missing: 17%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: overall_pearson\n  Percentage missing: 32%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'overall_spearman' %missing", 
-        "value": 0.16666666666666663, 
-        "severity": 1, 
-        "severity_value": 1.6666666666666663, 
+        "value": 0.32499999999999996, 
+        "severity": 3, 
+        "severity_value": 3.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: overall_spearman\n  Percentage missing: 17%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: overall_spearman\n  Percentage missing: 32%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'rmse' %missing", 
-        "value": 0.05555555555555558, 
-        "severity": 0, 
-        "severity_value": 0.5555555555555558, 
+        "value": 0.21250000000000002, 
+        "severity": 2, 
+        "severity_value": 2.125, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: rmse\n  Percentage missing: 6%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: rmse\n  Percentage missing: 21%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Metric 'mae' %missing", 
-        "value": 0.05555555555555558, 
-        "severity": 0, 
-        "severity_value": 0.5555555555555558, 
+        "value": 0.21250000000000002, 
+        "severity": 2, 
+        "severity_value": 2.125, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mae\n  Percentage missing: 6%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  Metric id: mae\n  Percentage missing: 21%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -373,11 +373,11 @@
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Method 'knnr_py' %missing", 
-        "value": 0.125, 
-        "severity": 1, 
-        "severity_value": 1.25, 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: knnr_py\n  Percentage missing: 12%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: knnr_py\n  Percentage missing: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -393,21 +393,11 @@
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Method 'lm' %missing", 
-        "value": 0.125, 
-        "severity": 1, 
-        "severity_value": 1.25, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: lm\n  Percentage missing: 12%\n"
-    }, 
-    {
-        "task_id": "task_predict_modality", 
-        "category": "Raw results", 
-        "name": "Method 'lmds_irlba_rf' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "value": 0.21875, 
+        "severity": 2, 
+        "severity_value": 2.1875, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: lmds_irlba_rf\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: lm\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -422,82 +412,102 @@
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
-        "name": "Dataset 'openproblems_neurips2021/bmmc_cite/normal' %missing", 
-        "value": 0.02777777777777779, 
-        "severity": 0, 
-        "severity_value": 0.2777777777777779, 
+        "name": "Method 'novel' %missing", 
+        "value": 0.75, 
+        "severity": 3, 
+        "severity_value": 7.5, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_cite/normal\n  Percentage missing: 3%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: novel\n  Percentage missing: 75%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
-        "name": "Dataset 'openproblems_neurips2021/bmmc_multiome/normal' %missing", 
-        "value": 0.02777777777777779, 
-        "severity": 0, 
-        "severity_value": 0.2777777777777779, 
+        "name": "Method 'simple_mlp' %missing", 
+        "value": 1.0, 
+        "severity": 3, 
+        "severity_value": 10.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_multiome/normal\n  Percentage missing: 3%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  method id: simple_mlp\n  Percentage missing: 100%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Dataset 'openproblems_neurips2022/pbmc_multiome/swap' %missing", 
-        "value": 0.36111111111111116, 
+        "value": 0.32499999999999996, 
         "severity": 3, 
-        "severity_value": 3.6111111111111116, 
+        "severity_value": 3.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_multiome/swap\n  Percentage missing: 36%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_multiome/swap\n  Percentage missing: 32%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Dataset 'openproblems_neurips2021/bmmc_multiome/swap' %missing", 
-        "value": 0.02777777777777779, 
-        "severity": 0, 
-        "severity_value": 0.2777777777777779, 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_multiome/swap\n  Percentage missing: 3%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_multiome/swap\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
-        "name": "Dataset 'openproblems_neurips2022/pbmc_multiome/normal' %missing", 
-        "value": 0.13888888888888884, 
+        "name": "Dataset 'openproblems_neurips2021/bmmc_cite/normal' %missing", 
+        "value": 0.125, 
         "severity": 1, 
-        "severity_value": 1.3888888888888884, 
+        "severity_value": 1.25, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_multiome/normal\n  Percentage missing: 14%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_cite/normal\n  Percentage missing: 12%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Dataset 'openproblems_neurips2022/pbmc_cite/normal' %missing", 
-        "value": 0.02777777777777779, 
-        "severity": 0, 
-        "severity_value": 0.2777777777777779, 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_cite/normal\n  Percentage missing: 3%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_cite/normal\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Dataset 'openproblems_neurips2022/pbmc_cite/swap' %missing", 
-        "value": 0.02777777777777779, 
-        "severity": 0, 
-        "severity_value": 0.2777777777777779, 
+        "value": 0.32499999999999996, 
+        "severity": 3, 
+        "severity_value": 3.2499999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_cite/swap\n  Percentage missing: 3%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_cite/swap\n  Percentage missing: 32%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Raw results", 
         "name": "Dataset 'openproblems_neurips2021/bmmc_cite/swap' %missing", 
-        "value": 0.02777777777777779, 
-        "severity": 0, 
-        "severity_value": 0.2777777777777779, 
+        "value": 0.19999999999999996, 
+        "severity": 1, 
+        "severity_value": 1.9999999999999996, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_cite/swap\n  Percentage missing: 3%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_cite/swap\n  Percentage missing: 20%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_neurips2022/pbmc_multiome/normal' %missing", 
+        "value": 0.32499999999999996, 
+        "severity": 3, 
+        "severity_value": 3.2499999999999996, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2022/pbmc_multiome/normal\n  Percentage missing: 32%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_neurips2021/bmmc_multiome/normal' %missing", 
+        "value": 0.22499999999999998, 
+        "severity": 2, 
+        "severity_value": 2.2499999999999996, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_predict_modality\n  dataset id: openproblems_neurips2021/bmmc_multiome/normal\n  Percentage missing: 22%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -523,21 +533,21 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score random_predict mean_pearson_per_cell", 
-        "value": 0.0202, 
+        "value": 0.0211, 
         "severity": 0, 
-        "severity_value": -0.0202, 
+        "severity_value": -0.0211, 
         "code": "worst_score >= -1", 
-        "message": "Method random_predict performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0202%\n"
+        "message": "Method random_predict performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0211%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score random_predict mean_pearson_per_cell", 
-        "value": 0.7556, 
+        "value": 0.7542, 
         "severity": 0, 
-        "severity_value": 0.3778, 
+        "severity_value": 0.3771, 
         "code": "best_score <= 2", 
-        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_pearson_per_cell\n  Best score: 0.7556%\n"
+        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_pearson_per_cell\n  Best score: 0.7542%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -583,21 +593,21 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py mean_pearson_per_cell", 
-        "value": 0.0, 
+        "value": 0.161, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.161, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.161%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py mean_pearson_per_cell", 
-        "value": 0.8764, 
+        "value": 0.8766, 
         "severity": 0, 
-        "severity_value": 0.4382, 
+        "severity_value": 0.4383, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_cell\n  Best score: 0.8764%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_cell\n  Best score: 0.8766%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -613,11 +623,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r mean_pearson_per_cell", 
-        "value": 0.8737, 
+        "value": 0.8736, 
         "severity": 0, 
-        "severity_value": 0.43685, 
+        "severity_value": 0.4368, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_pearson_per_cell\n  Best score: 0.8737%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_pearson_per_cell\n  Best score: 0.8736%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -633,51 +643,71 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm mean_pearson_per_cell", 
-        "value": 0.7078, 
+        "value": 0.7043, 
         "severity": 0, 
-        "severity_value": 0.3539, 
+        "severity_value": 0.35215, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_pearson_per_cell\n  Best score: 0.7078%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_pearson_per_cell\n  Best score: 0.7043%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf mean_pearson_per_cell", 
-        "value": 0.0729, 
+        "name": "Worst score guanlab_dengkw_pm mean_pearson_per_cell", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": -0.0729, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0729%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf mean_pearson_per_cell", 
-        "value": 0.7061, 
+        "name": "Best score guanlab_dengkw_pm mean_pearson_per_cell", 
+        "value": 0.8844, 
         "severity": 0, 
-        "severity_value": 0.35305, 
+        "severity_value": 0.4422, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_pearson_per_cell\n  Best score: 0.7061%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_cell\n  Best score: 0.8844%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm mean_pearson_per_cell", 
+        "name": "Worst score novel mean_pearson_per_cell", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_pearson_per_cell\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm mean_pearson_per_cell", 
-        "value": 0.8843, 
+        "name": "Best score novel mean_pearson_per_cell", 
+        "value": 0.7762, 
         "severity": 0, 
-        "severity_value": 0.44215, 
+        "severity_value": 0.3881, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_cell\n  Best score: 0.8843%\n"
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_pearson_per_cell\n  Best score: 0.7762%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp mean_pearson_per_cell", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_pearson_per_cell\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp mean_pearson_per_cell", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_pearson_per_cell\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -703,21 +733,21 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score random_predict mean_spearman_per_cell", 
-        "value": 0.0723, 
+        "value": 0.0836, 
         "severity": 0, 
-        "severity_value": -0.0723, 
+        "severity_value": -0.0836, 
         "code": "worst_score >= -1", 
-        "message": "Method random_predict performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0723%\n"
+        "message": "Method random_predict performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0836%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score random_predict mean_spearman_per_cell", 
-        "value": 0.5185, 
+        "value": 0.5175, 
         "severity": 0, 
-        "severity_value": 0.25925, 
+        "severity_value": 0.25875, 
         "code": "best_score <= 2", 
-        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_spearman_per_cell\n  Best score: 0.5185%\n"
+        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_spearman_per_cell\n  Best score: 0.5175%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -763,41 +793,41 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py mean_spearman_per_cell", 
-        "value": 0.0, 
+        "value": 0.1966, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.1966, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.1966%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py mean_spearman_per_cell", 
-        "value": 0.6911, 
+        "value": 0.6917, 
         "severity": 0, 
-        "severity_value": 0.34555, 
+        "severity_value": 0.34585, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6911%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6917%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_r mean_spearman_per_cell", 
-        "value": 0.1941, 
+        "value": 0.194, 
         "severity": 0, 
-        "severity_value": -0.1941, 
+        "severity_value": -0.194, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.1941%\n"
+        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.194%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r mean_spearman_per_cell", 
-        "value": 0.6708, 
+        "value": 0.6703, 
         "severity": 0, 
-        "severity_value": 0.3354, 
+        "severity_value": 0.33515, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6708%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6703%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -813,51 +843,71 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm mean_spearman_per_cell", 
-        "value": 0.6098, 
+        "value": 0.6062, 
         "severity": 0, 
-        "severity_value": 0.3049, 
+        "severity_value": 0.3031, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6098%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6062%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf mean_spearman_per_cell", 
-        "value": 0.0228, 
+        "name": "Worst score guanlab_dengkw_pm mean_spearman_per_cell", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": -0.0228, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0228%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf mean_spearman_per_cell", 
-        "value": 0.589, 
+        "name": "Best score guanlab_dengkw_pm mean_spearman_per_cell", 
+        "value": 0.6864, 
         "severity": 0, 
-        "severity_value": 0.2945, 
+        "severity_value": 0.3432, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_spearman_per_cell\n  Best score: 0.589%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6864%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm mean_spearman_per_cell", 
+        "name": "Worst score novel mean_spearman_per_cell", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_spearman_per_cell\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm mean_spearman_per_cell", 
-        "value": 0.686, 
+        "name": "Best score novel mean_spearman_per_cell", 
+        "value": 0.6897, 
+        "severity": 0, 
+        "severity_value": 0.34485, 
+        "code": "best_score <= 2", 
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_spearman_per_cell\n  Best score: 0.6897%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp mean_spearman_per_cell", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_spearman_per_cell\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp mean_spearman_per_cell", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": 0.343, 
+        "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_cell\n  Best score: 0.686%\n"
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_spearman_per_cell\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -873,11 +923,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score mean_per_gene mean_pearson_per_gene", 
-        "value": 0.0157, 
+        "value": 0.0021, 
         "severity": 0, 
-        "severity_value": 0.00785, 
+        "severity_value": 0.00105, 
         "code": "best_score <= 2", 
-        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: mean_pearson_per_gene\n  Best score: 0.0157%\n"
+        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: mean_pearson_per_gene\n  Best score: 0.0021%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -893,11 +943,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score random_predict mean_pearson_per_gene", 
-        "value": 0.0034, 
+        "value": 0.0015, 
         "severity": 0, 
-        "severity_value": 0.0017, 
+        "severity_value": 0.00075, 
         "code": "best_score <= 2", 
-        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_pearson_per_gene\n  Best score: 0.0034%\n"
+        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_pearson_per_gene\n  Best score: 0.0015%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -913,11 +963,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score zeros mean_pearson_per_gene", 
-        "value": 0.0157, 
+        "value": 0.0021, 
         "severity": 0, 
-        "severity_value": 0.00785, 
+        "severity_value": 0.00105, 
         "code": "best_score <= 2", 
-        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: mean_pearson_per_gene\n  Best score: 0.0157%\n"
+        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: mean_pearson_per_gene\n  Best score: 0.0021%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -943,21 +993,21 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py mean_pearson_per_gene", 
-        "value": 0.0, 
+        "value": 0.0399, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.0399, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_gene\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_gene\n  Worst score: 0.0399%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py mean_pearson_per_gene", 
-        "value": 0.601, 
+        "value": 0.5944, 
         "severity": 0, 
-        "severity_value": 0.3005, 
+        "severity_value": 0.2972, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_gene\n  Best score: 0.601%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_pearson_per_gene\n  Best score: 0.5944%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -973,11 +1023,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r mean_pearson_per_gene", 
-        "value": 0.5439, 
+        "value": 0.5336, 
         "severity": 0, 
-        "severity_value": 0.27195, 
+        "severity_value": 0.2668, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_pearson_per_gene\n  Best score: 0.5439%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_pearson_per_gene\n  Best score: 0.5336%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -993,51 +1043,71 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm mean_pearson_per_gene", 
-        "value": 0.5264, 
+        "value": 0.5291, 
         "severity": 0, 
-        "severity_value": 0.2632, 
+        "severity_value": 0.26455, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_pearson_per_gene\n  Best score: 0.5264%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_pearson_per_gene\n  Best score: 0.5291%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf mean_pearson_per_gene", 
-        "value": 0.0619, 
+        "name": "Worst score guanlab_dengkw_pm mean_pearson_per_gene", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": -0.0619, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_pearson_per_gene\n  Worst score: 0.0619%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_gene\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf mean_pearson_per_gene", 
-        "value": 0.5398, 
+        "name": "Best score guanlab_dengkw_pm mean_pearson_per_gene", 
+        "value": 0.6415, 
         "severity": 0, 
-        "severity_value": 0.2699, 
+        "severity_value": 0.32075, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_pearson_per_gene\n  Best score: 0.5398%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_gene\n  Best score: 0.6415%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm mean_pearson_per_gene", 
+        "name": "Worst score novel mean_pearson_per_gene", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_gene\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_pearson_per_gene\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm mean_pearson_per_gene", 
-        "value": 0.6474, 
+        "name": "Best score novel mean_pearson_per_gene", 
+        "value": 0.4998, 
         "severity": 0, 
-        "severity_value": 0.3237, 
+        "severity_value": 0.2499, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_pearson_per_gene\n  Best score: 0.6474%\n"
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_pearson_per_gene\n  Best score: 0.4998%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp mean_pearson_per_gene", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_pearson_per_gene\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp mean_pearson_per_gene", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_pearson_per_gene\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1053,11 +1123,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score mean_per_gene mean_spearman_per_gene", 
-        "value": 0.0193, 
+        "value": 0.0063, 
         "severity": 0, 
-        "severity_value": 0.00965, 
+        "severity_value": 0.00315, 
         "code": "best_score <= 2", 
-        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: mean_spearman_per_gene\n  Best score: 0.0193%\n"
+        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: mean_spearman_per_gene\n  Best score: 0.0063%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1073,11 +1143,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score random_predict mean_spearman_per_gene", 
-        "value": 0.0009, 
+        "value": 0.0019, 
         "severity": 0, 
-        "severity_value": 0.00045, 
+        "severity_value": 0.00095, 
         "code": "best_score <= 2", 
-        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_spearman_per_gene\n  Best score: 0.0009%\n"
+        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mean_spearman_per_gene\n  Best score: 0.0019%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1093,11 +1163,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score zeros mean_spearman_per_gene", 
-        "value": 0.0193, 
+        "value": 0.0063, 
         "severity": 0, 
-        "severity_value": 0.00965, 
+        "severity_value": 0.00315, 
         "code": "best_score <= 2", 
-        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: mean_spearman_per_gene\n  Best score: 0.0193%\n"
+        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: mean_spearman_per_gene\n  Best score: 0.0063%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1123,41 +1193,41 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py mean_spearman_per_gene", 
-        "value": 0.0, 
+        "value": 0.0412, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.0412, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0412%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py mean_spearman_per_gene", 
-        "value": 0.499, 
+        "value": 0.4887, 
         "severity": 0, 
-        "severity_value": 0.2495, 
+        "severity_value": 0.24435, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_gene\n  Best score: 0.499%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mean_spearman_per_gene\n  Best score: 0.4887%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_r mean_spearman_per_gene", 
-        "value": 0.0208, 
+        "value": 0.021, 
         "severity": 0, 
-        "severity_value": -0.0208, 
+        "severity_value": -0.021, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0208%\n"
+        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.021%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r mean_spearman_per_gene", 
-        "value": 0.4419, 
+        "value": 0.4277, 
         "severity": 0, 
-        "severity_value": 0.22095, 
+        "severity_value": 0.21385, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_gene\n  Best score: 0.4419%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mean_spearman_per_gene\n  Best score: 0.4277%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1173,71 +1243,91 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm mean_spearman_per_gene", 
-        "value": 0.4398, 
+        "value": 0.4338, 
         "severity": 0, 
-        "severity_value": 0.2199, 
+        "severity_value": 0.2169, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_spearman_per_gene\n  Best score: 0.4398%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mean_spearman_per_gene\n  Best score: 0.4338%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf mean_spearman_per_gene", 
-        "value": 0.0631, 
+        "name": "Worst score guanlab_dengkw_pm mean_spearman_per_gene", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": -0.0631, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0631%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf mean_spearman_per_gene", 
-        "value": 0.4441, 
+        "name": "Best score guanlab_dengkw_pm mean_spearman_per_gene", 
+        "value": 0.5036, 
         "severity": 0, 
-        "severity_value": 0.22205, 
+        "severity_value": 0.2518, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mean_spearman_per_gene\n  Best score: 0.4441%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_gene\n  Best score: 0.5036%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm mean_spearman_per_gene", 
+        "name": "Worst score novel mean_spearman_per_gene", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_spearman_per_gene\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm mean_spearman_per_gene", 
-        "value": 0.5135, 
+        "name": "Best score novel mean_spearman_per_gene", 
+        "value": 0.3719, 
         "severity": 0, 
-        "severity_value": 0.25675, 
+        "severity_value": 0.18595, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mean_spearman_per_gene\n  Best score: 0.5135%\n"
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mean_spearman_per_gene\n  Best score: 0.3719%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp mean_spearman_per_gene", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_spearman_per_gene\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp mean_spearman_per_gene", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mean_spearman_per_gene\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score mean_per_gene overall_pearson", 
-        "value": 0.1203, 
+        "value": 0.1192, 
         "severity": 0, 
-        "severity_value": -0.1203, 
+        "severity_value": -0.1192, 
         "code": "worst_score >= -1", 
-        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_pearson\n  Worst score: 0.1203%\n"
+        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_pearson\n  Worst score: 0.1192%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score mean_per_gene overall_pearson", 
-        "value": 0.4197, 
+        "value": 0.4135, 
         "severity": 0, 
-        "severity_value": 0.20985, 
+        "severity_value": 0.20675, 
         "code": "best_score <= 2", 
-        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_pearson\n  Best score: 0.4197%\n"
+        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_pearson\n  Best score: 0.4135%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1303,121 +1393,141 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py overall_pearson", 
-        "value": 0.0, 
+        "value": 0.1393, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.1393, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_pearson\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_pearson\n  Worst score: 0.1393%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py overall_pearson", 
-        "value": 0.7082, 
+        "value": 0.7075, 
         "severity": 0, 
-        "severity_value": 0.3541, 
+        "severity_value": 0.35375, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_pearson\n  Best score: 0.7082%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_pearson\n  Best score: 0.7075%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_r overall_pearson", 
-        "value": 0.0864, 
+        "value": 0.0858, 
         "severity": 0, 
-        "severity_value": -0.0864, 
+        "severity_value": -0.0858, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_pearson\n  Worst score: 0.0864%\n"
+        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_pearson\n  Worst score: 0.0858%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r overall_pearson", 
-        "value": 0.6654, 
+        "value": 0.6596, 
         "severity": 0, 
-        "severity_value": 0.3327, 
+        "severity_value": 0.3298, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_pearson\n  Best score: 0.6654%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_pearson\n  Best score: 0.6596%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score lm overall_pearson", 
-        "value": -0.6151, 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": 0.6151, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_pearson\n  Worst score: -0.6151%\n"
+        "message": "Method lm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_pearson\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm overall_pearson", 
-        "value": 0.6541, 
+        "value": 0.6593, 
         "severity": 0, 
-        "severity_value": 0.32705, 
+        "severity_value": 0.32965, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_pearson\n  Best score: 0.6541%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_pearson\n  Best score: 0.6593%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf overall_pearson", 
-        "value": -2.4102, 
-        "severity": 2, 
-        "severity_value": 2.4102, 
+        "name": "Worst score guanlab_dengkw_pm overall_pearson", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: overall_pearson\n  Worst score: -2.4102%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_pearson\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf overall_pearson", 
-        "value": 0.6371, 
+        "name": "Best score guanlab_dengkw_pm overall_pearson", 
+        "value": 0.7476, 
         "severity": 0, 
-        "severity_value": 0.31855, 
+        "severity_value": 0.3738, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: overall_pearson\n  Best score: 0.6371%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_pearson\n  Best score: 0.7476%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm overall_pearson", 
+        "name": "Worst score novel overall_pearson", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_pearson\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: overall_pearson\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm overall_pearson", 
-        "value": 0.7483, 
+        "name": "Best score novel overall_pearson", 
+        "value": 0.6401, 
         "severity": 0, 
-        "severity_value": 0.37415, 
+        "severity_value": 0.32005, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_pearson\n  Best score: 0.7483%\n"
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: overall_pearson\n  Best score: 0.6401%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp overall_pearson", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: overall_pearson\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp overall_pearson", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: overall_pearson\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score mean_per_gene overall_spearman", 
-        "value": 0.0908, 
+        "value": 0.0731, 
         "severity": 0, 
-        "severity_value": -0.0908, 
+        "severity_value": -0.0731, 
         "code": "worst_score >= -1", 
-        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_spearman\n  Worst score: 0.0908%\n"
+        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_spearman\n  Worst score: 0.0731%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score mean_per_gene overall_spearman", 
-        "value": 0.1824, 
+        "value": 0.1815, 
         "severity": 0, 
-        "severity_value": 0.0912, 
+        "severity_value": 0.09075, 
         "code": "best_score <= 2", 
-        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_spearman\n  Best score: 0.1824%\n"
+        "message": "Method mean_per_gene performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: overall_spearman\n  Best score: 0.1815%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1483,111 +1593,131 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py overall_spearman", 
-        "value": 0.0, 
+        "value": 0.1098, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.1098, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_spearman\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_spearman\n  Worst score: 0.1098%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py overall_spearman", 
-        "value": 0.61, 
+        "value": 0.6025, 
         "severity": 0, 
-        "severity_value": 0.305, 
+        "severity_value": 0.30125, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_spearman\n  Best score: 0.61%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: overall_spearman\n  Best score: 0.6025%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_r overall_spearman", 
-        "value": 0.1103, 
+        "value": 0.1057, 
         "severity": 0, 
-        "severity_value": -0.1103, 
+        "severity_value": -0.1057, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_spearman\n  Worst score: 0.1103%\n"
+        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_spearman\n  Worst score: 0.1057%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r overall_spearman", 
-        "value": 0.5663, 
+        "value": 0.5548, 
         "severity": 0, 
-        "severity_value": 0.28315, 
+        "severity_value": 0.2774, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_spearman\n  Best score: 0.5663%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: overall_spearman\n  Best score: 0.5548%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score lm overall_spearman", 
-        "value": -0.2449, 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": 0.2449, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_spearman\n  Worst score: -0.2449%\n"
+        "message": "Method lm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_spearman\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm overall_spearman", 
-        "value": 0.5364, 
+        "value": 0.5312, 
         "severity": 0, 
-        "severity_value": 0.2682, 
+        "severity_value": 0.2656, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_spearman\n  Best score: 0.5364%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: overall_spearman\n  Best score: 0.5312%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf overall_spearman", 
-        "value": -0.9923, 
+        "name": "Worst score guanlab_dengkw_pm overall_spearman", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": 0.9923, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: overall_spearman\n  Worst score: -0.9923%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_spearman\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf overall_spearman", 
-        "value": 0.5143, 
+        "name": "Best score guanlab_dengkw_pm overall_spearman", 
+        "value": 0.6158, 
         "severity": 0, 
-        "severity_value": 0.25715, 
+        "severity_value": 0.3079, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: overall_spearman\n  Best score: 0.5143%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_spearman\n  Best score: 0.6158%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm overall_spearman", 
+        "name": "Worst score novel overall_spearman", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_spearman\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: overall_spearman\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm overall_spearman", 
-        "value": 0.6234, 
+        "name": "Best score novel overall_spearman", 
+        "value": 0.5182, 
+        "severity": 0, 
+        "severity_value": 0.2591, 
+        "code": "best_score <= 2", 
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: overall_spearman\n  Best score: 0.5182%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp overall_spearman", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": 0.3117, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: overall_spearman\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp overall_spearman", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: overall_spearman\n  Best score: 0.6234%\n"
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: overall_spearman\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score mean_per_gene rmse", 
-        "value": 0.2007, 
+        "value": 0.1982, 
         "severity": 0, 
-        "severity_value": -0.2007, 
+        "severity_value": -0.1982, 
         "code": "worst_score >= -1", 
-        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: rmse\n  Worst score: 0.2007%\n"
+        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: rmse\n  Worst score: 0.1982%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1613,11 +1743,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score random_predict rmse", 
-        "value": 0.3408, 
+        "value": 0.3387, 
         "severity": 0, 
-        "severity_value": 0.1704, 
+        "severity_value": 0.16935, 
         "code": "best_score <= 2", 
-        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: rmse\n  Best score: 0.3408%\n"
+        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: rmse\n  Best score: 0.3387%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1633,11 +1763,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score zeros rmse", 
-        "value": 0.3061, 
+        "value": 0.3057, 
         "severity": 0, 
-        "severity_value": 0.15305, 
+        "severity_value": 0.15285, 
         "code": "best_score <= 2", 
-        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: rmse\n  Best score: 0.3061%\n"
+        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: rmse\n  Best score: 0.3057%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1663,41 +1793,41 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py rmse", 
-        "value": 0.0, 
+        "value": 0.2749, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.2749, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: rmse\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: rmse\n  Worst score: 0.2749%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_py rmse", 
-        "value": 0.5465, 
+        "value": 0.5469, 
         "severity": 0, 
-        "severity_value": 0.27325, 
+        "severity_value": 0.27345, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: rmse\n  Best score: 0.5465%\n"
+        "message": "Method knnr_py performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: rmse\n  Best score: 0.5469%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_r rmse", 
-        "value": 0.2044, 
+        "value": 0.2035, 
         "severity": 0, 
-        "severity_value": -0.2044, 
+        "severity_value": -0.2035, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: rmse\n  Worst score: 0.2044%\n"
+        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: rmse\n  Worst score: 0.2035%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r rmse", 
-        "value": 0.5417, 
+        "value": 0.5415, 
         "severity": 0, 
-        "severity_value": 0.27085, 
+        "severity_value": 0.27075, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: rmse\n  Best score: 0.5417%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: rmse\n  Best score: 0.5415%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1713,61 +1843,81 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm rmse", 
-        "value": 0.4537, 
+        "value": 0.4538, 
         "severity": 0, 
-        "severity_value": 0.22685, 
+        "severity_value": 0.2269, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: rmse\n  Best score: 0.4537%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: rmse\n  Best score: 0.4538%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf rmse", 
-        "value": 0.0089, 
+        "name": "Worst score guanlab_dengkw_pm rmse", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": -0.0089, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: rmse\n  Worst score: 0.0089%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: rmse\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf rmse", 
-        "value": 0.3061, 
+        "name": "Best score guanlab_dengkw_pm rmse", 
+        "value": 0.5611, 
         "severity": 0, 
-        "severity_value": 0.15305, 
+        "severity_value": 0.28055, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: rmse\n  Best score: 0.3061%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: rmse\n  Best score: 0.5611%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm rmse", 
+        "name": "Worst score novel rmse", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: rmse\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: rmse\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm rmse", 
-        "value": 0.561, 
+        "name": "Best score novel rmse", 
+        "value": 0.3883, 
+        "severity": 0, 
+        "severity_value": 0.19415, 
+        "code": "best_score <= 2", 
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: rmse\n  Best score: 0.3883%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp rmse", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": 0.2805, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: rmse\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp rmse", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: rmse\n  Best score: 0.561%\n"
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: rmse\n  Best score: 0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score mean_per_gene mae", 
-        "value": 0.0259, 
+        "value": 0.023, 
         "severity": 0, 
-        "severity_value": -0.0259, 
+        "severity_value": -0.023, 
         "code": "worst_score >= -1", 
-        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: mae\n  Worst score: 0.0259%\n"
+        "message": "Method mean_per_gene performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: mean_per_gene\n  Metric id: mae\n  Worst score: 0.023%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1793,11 +1943,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score random_predict mae", 
-        "value": 0.2591, 
+        "value": 0.2771, 
         "severity": 0, 
-        "severity_value": 0.12955, 
+        "severity_value": 0.13855, 
         "code": "best_score <= 2", 
-        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mae\n  Best score: 0.2591%\n"
+        "message": "Method random_predict performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: random_predict\n  Metric id: mae\n  Best score: 0.2771%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1813,11 +1963,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score zeros mae", 
-        "value": 0.46, 
+        "value": 0.4613, 
         "severity": 0, 
-        "severity_value": 0.23, 
+        "severity_value": 0.23065, 
         "code": "best_score <= 2", 
-        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: mae\n  Best score: 0.46%\n"
+        "message": "Method zeros performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: zeros\n  Metric id: mae\n  Best score: 0.4613%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1843,11 +1993,11 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_py mae", 
-        "value": 0.0, 
+        "value": 0.0606, 
         "severity": 0, 
-        "severity_value": -0.0, 
+        "severity_value": -0.0606, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mae\n  Worst score: 0.0%\n"
+        "message": "Method knnr_py performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_py\n  Metric id: mae\n  Worst score: 0.0606%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1863,21 +2013,21 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Worst score knnr_r mae", 
-        "value": 0.0167, 
+        "value": 0.0329, 
         "severity": 0, 
-        "severity_value": -0.0167, 
+        "severity_value": -0.0329, 
         "code": "worst_score >= -1", 
-        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mae\n  Worst score: 0.0167%\n"
+        "message": "Method knnr_r performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mae\n  Worst score: 0.0329%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score knnr_r mae", 
-        "value": 0.5132, 
+        "value": 0.5129, 
         "severity": 0, 
-        "severity_value": 0.2566, 
+        "severity_value": 0.25645, 
         "code": "best_score <= 2", 
-        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mae\n  Best score: 0.5132%\n"
+        "message": "Method knnr_r performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: knnr_r\n  Metric id: mae\n  Best score: 0.5129%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
@@ -1893,50 +2043,70 @@
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
         "name": "Best score lm mae", 
-        "value": 0.4771, 
+        "value": 0.4734, 
         "severity": 0, 
-        "severity_value": 0.23855, 
+        "severity_value": 0.2367, 
         "code": "best_score <= 2", 
-        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mae\n  Best score: 0.4771%\n"
+        "message": "Method lm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lm\n  Metric id: mae\n  Best score: 0.4734%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score lmds_irlba_rf mae", 
-        "value": -0.0588, 
+        "name": "Worst score guanlab_dengkw_pm mae", 
+        "value": 0.0, 
         "severity": 0, 
-        "severity_value": 0.0588, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method lmds_irlba_rf performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mae\n  Worst score: -0.0588%\n"
+        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mae\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score lmds_irlba_rf mae", 
-        "value": 0.278, 
+        "name": "Best score guanlab_dengkw_pm mae", 
+        "value": 0.5128, 
         "severity": 0, 
-        "severity_value": 0.139, 
+        "severity_value": 0.2564, 
         "code": "best_score <= 2", 
-        "message": "Method lmds_irlba_rf performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: lmds_irlba_rf\n  Metric id: mae\n  Best score: 0.278%\n"
+        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mae\n  Best score: 0.5128%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Worst score guanlab_dengkw_pm mae", 
+        "name": "Worst score novel mae", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method guanlab_dengkw_pm performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mae\n  Worst score: 0.0%\n"
+        "message": "Method novel performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mae\n  Worst score: 0.0%\n"
     }, 
     {
         "task_id": "task_predict_modality", 
         "category": "Scaling", 
-        "name": "Best score guanlab_dengkw_pm mae", 
-        "value": 0.5124, 
+        "name": "Best score novel mae", 
+        "value": 0.414, 
         "severity": 0, 
-        "severity_value": 0.2562, 
+        "severity_value": 0.207, 
+        "code": "best_score <= 2", 
+        "message": "Method novel performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: novel\n  Metric id: mae\n  Best score: 0.414%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Worst score simple_mlp mae", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method simple_mlp performs much worse than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mae\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_predict_modality", 
+        "category": "Scaling", 
+        "name": "Best score simple_mlp mae", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method guanlab_dengkw_pm performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: guanlab_dengkw_pm\n  Metric id: mae\n  Best score: 0.5124%\n"
+        "message": "Method simple_mlp performs a lot better than baselines.\n  Task id: task_predict_modality\n  Method id: simple_mlp\n  Metric id: mae\n  Best score: 0%\n"
     }
 ]
\ No newline at end of file
diff --git a/results/predict_modality/data/results.json b/results/predict_modality/data/results.json
index ad3cf37f..ca572738 100644
--- a/results/predict_modality/data/results.json
+++ b/results/predict_modality/data/results.json
@@ -3,33 +3,33 @@
     "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
     "method_id": "guanlab_dengkw_pm",
     "metric_values": {
-      "mae": 0.5442,
-      "mean_pearson_per_cell": 0.787,
-      "mean_pearson_per_gene": 0.6418,
-      "mean_spearman_per_cell": 0.686,
-      "mean_spearman_per_gene": 0.5039,
-      "overall_pearson": 0.7518,
-      "overall_spearman": 0.6366,
-      "rmse": 0.7305
+      "mae": 0.5445,
+      "mean_pearson_per_cell": 0.7871,
+      "mean_pearson_per_gene": 0.6415,
+      "mean_spearman_per_cell": 0.6864,
+      "mean_spearman_per_gene": 0.5036,
+      "overall_pearson": 0.7519,
+      "overall_spearman": 0.6364,
+      "rmse": 0.7312
     },
     "scaled_scores": {
-      "mae": 0.466,
-      "mean_pearson_per_cell": 0.787,
-      "mean_pearson_per_gene": 0.6474,
-      "mean_spearman_per_cell": 0.686,
-      "mean_spearman_per_gene": 0.5135,
-      "overall_pearson": 0.7483,
-      "overall_spearman": 0.6234,
-      "rmse": 0.4548
-    },
-    "mean_score": 0.6158,
+      "mae": 0.4657,
+      "mean_pearson_per_cell": 0.7871,
+      "mean_pearson_per_gene": 0.6415,
+      "mean_spearman_per_cell": 0.6864,
+      "mean_spearman_per_gene": 0.5036,
+      "overall_pearson": 0.7476,
+      "overall_spearman": 0.6158,
+      "rmse": 0.4543
+    },
+    "mean_score": 0.6128,
     "resources": {
-      "submit": "2024-11-25 13:44:25",
+      "submit": "2025-01-09 15:35:20",
       "exit_code": 0,
-      "duration_sec": 588,
-      "cpu_pct": 406,
+      "duration_sec": 1285,
+      "cpu_pct": 557.5,
       "peak_memory_mb": 46285,
-      "disk_read_mb": 715,
+      "disk_read_mb": 716,
       "disk_write_mb": 1
     }
   },
@@ -37,33 +37,33 @@
     "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
     "method_id": "knnr_py",
     "metric_values": {
-      "mae": 0.5223,
-      "mean_pearson_per_cell": 0.7772,
-      "mean_pearson_per_gene": 0.5946,
-      "mean_spearman_per_cell": 0.6911,
-      "mean_spearman_per_gene": 0.4891,
-      "overall_pearson": 0.7122,
-      "overall_spearman": 0.6237,
-      "rmse": 0.7245
+      "mae": 0.5215,
+      "mean_pearson_per_cell": 0.7775,
+      "mean_pearson_per_gene": 0.5944,
+      "mean_spearman_per_cell": 0.6917,
+      "mean_spearman_per_gene": 0.4887,
+      "overall_pearson": 0.7124,
+      "overall_spearman": 0.6239,
+      "rmse": 0.7236
     },
     "scaled_scores": {
-      "mae": 0.4875,
-      "mean_pearson_per_cell": 0.7772,
-      "mean_pearson_per_gene": 0.601,
-      "mean_spearman_per_cell": 0.6911,
-      "mean_spearman_per_gene": 0.499,
-      "overall_pearson": 0.7082,
-      "overall_spearman": 0.61,
-      "rmse": 0.4593
-    },
-    "mean_score": 0.6042,
+      "mae": 0.4883,
+      "mean_pearson_per_cell": 0.7775,
+      "mean_pearson_per_gene": 0.5944,
+      "mean_spearman_per_cell": 0.6917,
+      "mean_spearman_per_gene": 0.4887,
+      "overall_pearson": 0.7075,
+      "overall_spearman": 0.6025,
+      "rmse": 0.46
+    },
+    "mean_score": 0.6013,
     "resources": {
-      "submit": "2024-11-25 13:44:25",
+      "submit": "2025-01-09 15:35:20",
       "exit_code": 0,
-      "duration_sec": 51.4,
-      "cpu_pct": 315.1,
-      "peak_memory_mb": 10138,
-      "disk_read_mb": 715,
+      "duration_sec": 93,
+      "cpu_pct": 159.3,
+      "peak_memory_mb": 10036,
+      "disk_read_mb": 717,
       "disk_write_mb": 1
     }
   },
@@ -71,33 +71,33 @@
     "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
     "method_id": "knnr_r",
     "metric_values": {
-      "mae": 0.5593,
-      "mean_pearson_per_cell": 0.7563,
-      "mean_pearson_per_gene": 0.5367,
-      "mean_spearman_per_cell": 0.6708,
-      "mean_spearman_per_gene": 0.4309,
-      "overall_pearson": 0.67,
-      "overall_spearman": 0.5816,
-      "rmse": 0.7776
-    },
-    "scaled_scores": {
-      "mae": 0.4512,
-      "mean_pearson_per_cell": 0.7563,
-      "mean_pearson_per_gene": 0.5439,
-      "mean_spearman_per_cell": 0.6708,
-      "mean_spearman_per_gene": 0.4419,
+      "mae": 0.5635,
+      "mean_pearson_per_cell": 0.7548,
+      "mean_pearson_per_gene": 0.5336,
+      "mean_spearman_per_cell": 0.6703,
+      "mean_spearman_per_gene": 0.4277,
       "overall_pearson": 0.6654,
-      "overall_spearman": 0.5663,
-      "rmse": 0.4197
+      "overall_spearman": 0.5787,
+      "rmse": 0.7828
     },
-    "mean_score": 0.5644,
+    "scaled_scores": {
+      "mae": 0.447,
+      "mean_pearson_per_cell": 0.7548,
+      "mean_pearson_per_gene": 0.5336,
+      "mean_spearman_per_cell": 0.6703,
+      "mean_spearman_per_gene": 0.4277,
+      "overall_pearson": 0.6596,
+      "overall_spearman": 0.5548,
+      "rmse": 0.4158
+    },
+    "mean_score": 0.558,
     "resources": {
-      "submit": "2024-11-25 13:44:25",
+      "submit": "2025-01-09 15:35:20",
       "exit_code": 0,
-      "duration_sec": 80,
-      "cpu_pct": 105.3,
+      "duration_sec": 97,
+      "cpu_pct": 119.3,
       "peak_memory_mb": 11981,
-      "disk_read_mb": 718,
+      "disk_read_mb": 719,
       "disk_write_mb": 1
     }
   },
@@ -105,31 +105,31 @@
     "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
     "method_id": "lm",
     "metric_values": {
-      "mae": 0.566,
-      "mean_pearson_per_cell": 0.7078,
-      "mean_pearson_per_gene": 0.5188,
-      "mean_spearman_per_cell": 0.6098,
-      "mean_spearman_per_gene": 0.4288,
-      "overall_pearson": 0.6589,
-      "overall_spearman": 0.5528,
-      "rmse": 0.7754
+      "mae": 0.5583,
+      "mean_pearson_per_cell": 0.7043,
+      "mean_pearson_per_gene": 0.5291,
+      "mean_spearman_per_cell": 0.6062,
+      "mean_spearman_per_gene": 0.4338,
+      "overall_pearson": 0.665,
+      "overall_spearman": 0.5564,
+      "rmse": 0.7622
     },
     "scaled_scores": {
-      "mae": 0.4446,
-      "mean_pearson_per_cell": 0.7078,
-      "mean_pearson_per_gene": 0.5264,
-      "mean_spearman_per_cell": 0.6098,
-      "mean_spearman_per_gene": 0.4398,
-      "overall_pearson": 0.6541,
-      "overall_spearman": 0.5364,
-      "rmse": 0.4213
-    },
-    "mean_score": 0.5425,
+      "mae": 0.4522,
+      "mean_pearson_per_cell": 0.7043,
+      "mean_pearson_per_gene": 0.5291,
+      "mean_spearman_per_cell": 0.6062,
+      "mean_spearman_per_gene": 0.4338,
+      "overall_pearson": 0.6593,
+      "overall_spearman": 0.5312,
+      "rmse": 0.4312
+    },
+    "mean_score": 0.5434,
     "resources": {
-      "submit": "2024-11-25 13:44:25",
+      "submit": "2025-01-09 15:35:20",
       "exit_code": 0,
-      "duration_sec": 125,
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@@ -151,54 +151,88 @@
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+    "dataset_id": "openproblems_neurips2021/bmmc_cite/normal",
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@@ -228,10 +262,10 @@
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@@ -253,20 +287,20 @@
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@@ -275,33 +309,33 @@
     "dataset_id": "openproblems_neurips2021/bmmc_cite/swap",
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@@ -309,33 +343,33 @@
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@@ -343,32 +377,32 @@
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@@ -377,30 +411,30 @@
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+      "mean_pearson_per_cell": "NA",
+      "mean_pearson_per_gene": "NA",
+      "mean_spearman_per_cell": "NA",
+      "mean_spearman_per_gene": "NA",
+      "overall_pearson": "NA",
+      "overall_spearman": "NA",
+      "rmse": 0.4089
+    },
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@@ -421,56 +455,90 @@
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@@ -500,12 +568,12 @@
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@@ -523,23 +591,23 @@
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@@ -547,32 +615,32 @@
     "dataset_id": "openproblems_neurips2021/bmmc_multiome/normal",
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@@ -581,32 +649,32 @@
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@@ -615,32 +683,32 @@
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@@ -649,31 +717,31 @@
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@@ -693,21 +761,21 @@
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@@ -715,35 +783,69 @@
   },
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+    }
+  },
+  {
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@@ -772,10 +874,10 @@
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@@ -795,22 +897,22 @@
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@@ -819,31 +921,31 @@
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@@ -853,32 +955,32 @@
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@@ -887,32 +989,32 @@
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@@ -921,32 +1023,32 @@
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@@ -965,56 +1067,90 @@
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+  {
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+    "metric_values": {
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+      "mean_pearson_per_cell": "NA",
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+      "disk_write_mb": "NA"
+    }
+  },
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     "dataset_id": "openproblems_neurips2021/bmmc_multiome/swap",
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@@ -1044,10 +1180,10 @@
     },
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@@ -1067,23 +1203,23 @@
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@@ -1091,32 +1227,32 @@
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@@ -1127,30 +1263,30 @@
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@@ -1159,32 +1295,32 @@
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@@ -1193,32 +1329,32 @@
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@@ -1239,54 +1375,88 @@
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+  {
+    "dataset_id": "openproblems_neurips2022/pbmc_cite/normal",
+    "method_id": "novel",
+    "metric_values": {
+      "mae": "NA",
+      "mean_pearson_per_cell": "NA",
+      "mean_pearson_per_gene": "NA",
+      "mean_spearman_per_cell": "NA",
+      "mean_spearman_per_gene": "NA",
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+      "overall_spearman": "NA",
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+    },
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+    }
+  },
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@@ -1316,11 +1486,11 @@
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@@ -1341,21 +1511,21 @@
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@@ -1364,31 +1534,31 @@
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@@ -1398,31 +1568,31 @@
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@@ -1465,34 +1635,34 @@
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@@ -1588,11 +1792,11 @@
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+  },
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+  },
+  {
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+  {
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+  },
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+    }
+  },
+  {
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+      "mae": "NA",
+      "mean_pearson_per_cell": "NA",
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+      "overall_spearman": "NA"
+    },
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+      "disk_write_mb": "NA"
+    }
+  },
+  {
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+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2022/pbmc_cite/normal",
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+      "mae": "NA",
+      "mean_pearson_per_cell": "NA",
+      "mean_spearman_per_cell": "NA",
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+      "overall_spearman": "NA"
+    },
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+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2022/pbmc_cite/swap",
+    "method_id": "simple_mlp",
+    "metric_values": {
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+      "mae": "NA",
+      "mean_pearson_per_cell": "NA",
+      "mean_spearman_per_cell": "NA",
+      "mean_pearson_per_gene": "NA",
+      "mean_spearman_per_gene": "NA",
+      "overall_pearson": "NA",
+      "overall_spearman": "NA"
+    },
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+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2022/pbmc_multiome/normal",
+    "method_id": "simple_mlp",
+    "metric_values": {
+      "rmse": "NA",
+      "mae": "NA",
+      "mean_pearson_per_cell": "NA",
+      "mean_spearman_per_cell": "NA",
+      "mean_pearson_per_gene": "NA",
+      "mean_spearman_per_gene": "NA",
+      "overall_pearson": "NA",
+      "overall_spearman": "NA"
+    },
+    "scaled_scores": {
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+      "overall_spearman": 0
+    },
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+      "exit_code": 1,
+      "duration_sec": 70.3,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_neurips2022/pbmc_multiome/swap",
+    "method_id": "simple_mlp",
+    "metric_values": {
+      "rmse": "NA",
+      "mae": "NA",
+      "mean_pearson_per_cell": "NA",
+      "mean_spearman_per_cell": "NA",
+      "mean_pearson_per_gene": "NA",
+      "mean_spearman_per_gene": "NA",
+      "overall_pearson": "NA",
+      "overall_spearman": "NA"
+    },
+    "scaled_scores": {
+      "rmse": 0,
+      "mae": 0,
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+      "mean_spearman_per_cell": 0,
+      "mean_pearson_per_gene": 0,
+      "mean_spearman_per_gene": 0,
+      "overall_pearson": 0,
+      "overall_spearman": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2025-01-09 15:39:40",
+      "exit_code": 1,
+      "duration_sec": 200,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
   }
 ]
diff --git a/results/predict_modality/data/task_info.json b/results/predict_modality/data/task_info.json
index 72d050de..2da85b06 100644
--- a/results/predict_modality/data/task_info.json
+++ b/results/predict_modality/data/task_info.json
@@ -28,6 +28,14 @@
         "github": "Pseudomanifold"
       }
     },
+    {
+      "name": "Christopher Lance",
+      "roles": "author",
+      "info": {
+        "github": "xlancelottx",
+        "orcid": "0000-0002-1275-9802"
+      }
+    },
     {
       "name": "Daniel Burkhardt",
       "roles": "author",
@@ -65,6 +73,23 @@
         "github": "rcannood",
         "orcid": "0000-0003-3641-729X"
       }
+    },
+    {
+      "name": "Xueer Chen",
+      "roles": "contributor",
+      "info": {
+        "github": "xuerchen",
+        "email": "xc2579@columbia.edu"
+      }
+    },
+    {
+      "name": "Jiwei Liu",
+      "roles": "contributor",
+      "info": {
+        "github": "daxiongshu",
+        "email": "jiweil@nvidia.com",
+        "orcid": "0000-0002-8799-9763"
+      }
     }
   ],
   "version": "build_main",