Multilayer perceptron (log scran)
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 scran) on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8568004793289394 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8568529247812608 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.48583701291538395 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8760252989425833 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8777868450203619 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8148727355232758 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.9537462807681905 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.9546716872929825 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.7595492175376236 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.9851063829787234 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.9849520807815663 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8786949882795664 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.9208012326656394 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) 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 scran)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 | ||
| 0.925397640256357 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) 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 scran)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 | ||
| 0.6930725021224952 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) 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 scran)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 | ||
| 0.9285857572718155 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) 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 scran)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 | ||
| 0.9280688834551714 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) 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 scran)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 | ||
| 0.8914942538629572 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.23854937070195925 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.3068204107545123 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.1682215736679451 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8343000773395205 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 scran), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.8347189502833389 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 scran), metric(f1) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Configuration: Multilayer perceptron (log scran)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 | ||
| 0.6955124404833017 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 scran), metric(f1_macro) Source checking is not independent reproduction. |
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Related records
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- model: Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy
- model: Multilayer perceptron (log scran) 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 scran) 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 scran) 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 scran) 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 scran) 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 scran) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy
- model: Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score
- model: Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score
- model: Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- model: Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score
- model: Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score