Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
Label projection on Zebrafish (random split), Accuracy. Scored with Accuracy on Zebrafish (random 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).
Overview
Label projection on Zebrafish (random split), Accuracy. Scored with Accuracy on Zebrafish (random 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).
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Results
Each comparison retains its reviewed evaluation scope, dataset and metric. Results are shown without a pooled ranking.
Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
accuracy (fraction) · Higher values are better.
Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy · Zebrafish (random split) (Open Problems label projection split)
Evidence origin: Author-reported evaluation.
openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), metric(accuracy)- Results published by the Open Problems project, source checked but not independently reproduced.
- This covers the label projection task at v1.0.0 only, not the whole Open Problems suite.
- true_labels and random_labels are controls that bound the scale, not competing methods.
- Preprocessing is part of the run, so the same method appears once per parameter set.
Comparison details and limitations
Every method Open Problems label projection reports on Label projection on Zebrafish (random split), Accuracy, scored with Accuracy on Zebrafish (random split).
Automated source review: 2026-09-18. Numerical source review does not establish independent reproduction.
Dots show point estimates. Whiskers show only explicitly defined uncertainty (standard deviation, standard error or a labelled interval); their definitions remain in Table. Unresolved uncertainty is not plotted. Differences do not establish statistical significance.
Showing 12 of 16 matching rows.
- True Labels1
- Seurat reference mapping (SCTransform)0.86
- Logistic regression (log scran)0.843
- Logistic regression (log CP10k)0.843
- Multilayer perceptron (log CP10k)0.84
- Multilayer perceptron (log scran)0.834
- K-neighbors classifier (log CP10k)0.806
- XGBoost (log CP10k)0.803
- K-neighbors classifier (log scran)0.802
- XGBoost (log scran)0.788
- scANVI (Seurat v3 2000 HVG)0.779
- scANVI (All genes)0.752
Methods and evaluation design
Procedure, tasks and evaluated configurations
Evaluation design
Benchmarks bring together tasks and protocols. A task describes the biological question; a protocol defines a particular test.
Benchmarks
These source-backed links do not make different protocols or scores interchangeable.
Recorded evaluations
Each evaluation records what was tested and under which conditions.
- K-neighbors classifier (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- Logistic regression (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- Majority Vote on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- Random Labels on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- scANVI (All genes) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- scANVI (Seurat v3 2000 HVG) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- scArches+scANVI (All genes) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- scArches+scANVI (Seurat v3 2000 HVG) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
Run instructions
No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.
A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.
Strengths, limitations and unresolved questions
Evidence
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Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
1 evidence row matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Relationship: part of discovery-benchmark-open-problems Individual claims | openproblems-label primary benchmark evidence results, dataset(zebrafish_random), metric(accuracy) Version: v1.0.0 | source checked automated source review · 2026-09-18 Audit detailsPrimary-source transcription with no human sign-off and no independent reproduction. Field: Claim: open-problems-association-zebrafish-random-accuracy Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
Sources and history
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Release 2026-09-29-06401fd5b220 · Record review: source checked
1 source records and release history
Download this releaseTechnical metadata and extraction receipts
Stable ID: open-problems-task-zebrafish-random-accuracy
- areas
- cells-tissues
- tasks
- Label projection on Zebrafish (random split), Accuracy
- metric
- Accuracy
- metric direction
- higher
- dataset
- Zebrafish (random split)
- protocol
- 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).
- source locator
- results, dataset(zebrafish_random), metric(accuracy)
- comparison panels
- id: open-problems-panel-zebrafish-random-accuracy; title: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy; protocol id: open-problems-task-zebrafish-random-accuracy; dataset id: open-problems-dataset-zebrafish-random-split; metric: accuracy; unit: fraction; direction: higher; result ids: open-problems-result-true-labels-zebrafish-random-accuracy-accuracy; open-problems-result-majority-vote-zebrafish-random-accuracy-accuracy; open-problems-result-k-neighbors-classifier-log-cp10k-zebrafish-random-accuracy-accuracy; open-problems-result-logistic-regression-log-cp10k-zebrafish-random-accuracy-accuracy; open-problems-result-multilayer-perceptron-log-cp10k-zebrafish-random-accuracy-accuracy; open-problems-result-random-labels-zebrafish-random-accuracy-accuracy; open-problems-result-k-neighbors-classifier-log-scran-zebrafish-random-accuracy-accuracy; open-problems-result-logistic-regression-log-scran-zebrafish-random-accuracy-accuracy; open-problems-result-xgboost-log-cp10k-zebrafish-random-accuracy-accuracy; open-problems-result-multilayer-perceptron-log-scran-zebrafish-random-accuracy-accuracy; open-problems-result-seurat-reference-mapping-sctransform-zebrafish-random-accuracy-accuracy; open-problems-result-xgboost-log-scran-zebrafish-random-accuracy-accuracy; open-problems-result-scanvi-seurat-v3-2000-hvg-zebrafish-random-accuracy-accuracy; open-problems-result-scarches-plus-scanvi-all-genes-zebrafish-random-accuracy-accuracy; open-problems-result-scarches-plus-scanvi-seurat-v3-2000-hvg-zebrafish-random-accuracy-accuracy; open-problems-result-scanvi-all-genes-zebrafish-random-accuracy-accuracy; source ids: expansion-p3-open-problems; source locator: results, dataset(zebrafish_random), metric(accuracy); context: Every method Open Problems label projection reports on Label projection on Zebrafish (random split), Accuracy, scored with Accuracy on Zebrafish (random split).; caveats: Results published by the Open Problems project, source checked but not independently reproduced.; This covers the label projection task at v1.0.0 only, not the whole Open Problems suite.; true_labels and random_labels are controls that bound the scale, not competing methods.; Preprocessing is part of the run, so the same method appears once per parameter set.; review: method: automated_source_review; date: 2026-09-18
Related records
- part of: Open Problems
- subject: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: part of discovery-benchmark-open-problems
- benchmark: K-neighbors classifier (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Logistic regression (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Majority Vote on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Multilayer perceptron (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Multilayer perceptron (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Random Labels on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: scANVI (All genes) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: scANVI (Seurat v3 2000 HVG) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: scArches+scANVI (All genes) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: scArches+scANVI (Seurat v3 2000 HVG) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: True Labels on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: XGBoost (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- benchmark: XGBoost (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy