Multilayer perceptron (log CP10k)
MLP or "Multi-Layer Perceptron" is a type of artificial neural network that consists of multiple layers of interconnected neurons. Each neuron computes a weighted sum of all neurons in the previous layer and transforms it with nonlinear activation function. The output layer provides the final prediction, and network weights are updated by gradient descent to minimize the cross entropy loss. Here, the input data is 100-dimensional whitened PCA coordinates for each cell, and we use two hidden layers of 100 neurons each.
Overview
MLP or "Multi-Layer Perceptron" is a type of artificial neural network that consists of multiple layers of interconnected neurons. Each neuron computes a weighted sum of all neurons in the previous layer and transforms it with nonlinear activation function. The output layer provides the final prediction, and network weights are updated by gradient descent to minimize the cross entropy loss. Here, the input data is 100-dimensional whitened PCA coordinates for each cell, and we use two hidden layers of 100 neurons each.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
Release 2026-09-17-134cd1815de8 · 24 evaluations · 24 metric rows. Different protocols are not a single leaderboard. Where several source tables report the same metric, the published comparisons above offer a pooled view that names what it does not hold constant.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| Multilayer perceptron (log CP10k) on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), AccuracyDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8250449370880767 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 scoreDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.832031507991959 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 scoreDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.402588128174668 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), AccuracyDataset subset: CeNGEN (random split) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8575946239747011 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 scoreDataset subset: CeNGEN (random split) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8565399161183415 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 scoreDataset subset: CeNGEN (random split) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.7710990297473443 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9640248850419258 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 scoreDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9645805182050861 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 scoreDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8790308464162443 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), AccuracyDataset subset: Pancreas (random split) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.988145896656535 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 scoreDataset subset: Pancreas (random split) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9880554686775588 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 scoreDataset subset: Pancreas (random split) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9709369948626364 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), AccuracyDataset subset: Pancreas (random split with label noise) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8080123266563944 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 scoreDataset subset: Pancreas (random split with label noise) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8366856872231097 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 scoreDataset subset: Pancreas (random split with label noise) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.5463249016394928 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), AccuracyDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.931394182547643 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 scoreDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9309033200595019 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 scoreDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9072989997095794 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), AccuracyDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.23738160114181914 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 scoreDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.27619561215999017 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 scoreDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.20373028688647557 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), AccuracyDataset subset: Zebrafish (random split) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8397138437741686 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 scoreDataset subset: Zebrafish (random split) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8403436701669355 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 scoreDataset subset: Zebrafish (random split) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.7096306108366662 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(multilayer_perceptron), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
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Related records
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- model: Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy
- model: Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy
- model: Multilayer perceptron (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy
- model: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- model: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score
- model: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score