CPA* (scGPT)
Published model reimplemented or adapted by the PerturBench authors, as their asterisk marks.
Overview
Published model reimplemented or adapted by the PerturBench authors, as their asterisk marks.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
8 evaluations · 8 results. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: CPA* (scGPT) | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.70 ± 1 × 10 − 2 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.01 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourcePredict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(CPA ∗ (scGPT)), column(Cosine, log fold change (LogFC)) |
| Configuration: CPA* (scGPT) | Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Dataset subset: Norman19 (PerturBench split) | 0.064 ± 1 × 10 − 2 cosine_logfc_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.01 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (scGPT) on PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(CPA ∗ (scGPT)), column(Cosine, LogFC rank) |
| Configuration: CPA* (scGPT) | Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Dataset subset: Norman19 (PerturBench split) | 0.13 ± 2 × 10 − 2 rmse_mean_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.02 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (scGPT) on PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(CPA ∗ (scGPT)), column(RMSE, mean rank) |
| Configuration: CPA* (scGPT) | Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Dataset subset: Norman19 (PerturBench split) | 0.061 ± 2 × 10 − 3 rmse_mean error · lower Uncertainty: type: standard_deviation; value: 0.002 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (scGPT) on PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(CPA ∗ (scGPT)), column(RMSE, mean) |
| Configuration: CPA* (scGPT) | Task: PerturBench CT-COSINE: covariate transfer on Srivatsan20, Cosine similarity of log fold change Dataset subset: Srivatsan20 (PerturBench split) | 0.29 ± 9 × 10 − 4 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.0009 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceTrain on some cell types and predict drug effects in a held-out cell type, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(CPA ∗ (scGPT)), column(Cosine, log fold change (LogFC)) |
| Configuration: CPA* (scGPT) | Task: PerturBench CT-COSINE-RANK: covariate transfer on Srivatsan20, Cosine LogFC rank Dataset subset: Srivatsan20 (PerturBench split) | 0.38 ± 2 × 10 − 2 cosine_logfc_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.02 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (scGPT) on PerturBench CT-COSINE-RANK: covariate transfer on Srivatsan20, Cosine LogFC rank Train on some cell types and predict drug effects in a held-out cell type, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(CPA ∗ (scGPT)), column(Cosine, LogFC rank) |
| Configuration: CPA* (scGPT) | Task: PerturBench CT-RMSE-RANK: covariate transfer on Srivatsan20, RMSE mean rank Dataset subset: Srivatsan20 (PerturBench split) | 0.32 ± 1 × 10 − 2 rmse_mean_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.01 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (scGPT) on PerturBench CT-RMSE-RANK: covariate transfer on Srivatsan20, RMSE mean rank Train on some cell types and predict drug effects in a held-out cell type, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(CPA ∗ (scGPT)), column(RMSE, mean rank) |
| Configuration: CPA* (scGPT) | Task: PerturBench CT-RMSE: covariate transfer on Srivatsan20, RMSE of the mean Dataset subset: Srivatsan20 (PerturBench split) | 0.021 ± 3 × 10 − 4 rmse_mean error · lower Uncertainty: type: standard_deviation; value: 0.0003 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (scGPT) on PerturBench CT-RMSE: covariate transfer on Srivatsan20, RMSE of the mean Train on some cell types and predict drug effects in a held-out cell type, reported as the mean and one standard deviation over seeds. Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(CPA ∗ (scGPT)), column(RMSE, mean) |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
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How it works, versions and access
Underlying model: scGPT. Results on this page belong to this configuration and its evaluated settings.
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: uses model catalog-model-scgpt Individual claims | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Section 3.2 Models, paragraph S3.SS2.p1.1 and Appendix E.5 scGPT Embeddings Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | source checked automated source review · 2026-09-23 Audit detailsSource review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. CPA* is fed scGPT embeddings; this is a downstream perturbation pipeline, not a scGPT checkpoint. Field: Claim: model-evaluation-identity-801d4c7c01690df0754b 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
- PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Original source · Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256
Technical metadata and extraction receipts
Stable ID: perturbench-method-cpa-scgpt
- areas
- cells-tissues
- source locator
- Table 2, row(CPA ∗ (scGPT))
- missing metadata
- checkpoint revision: unreported; parameters: unextracted
Related records
- uses model: scGPT
- subject: CPA* (scGPT): uses model scGPT
- model: CPA* (scGPT) on PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change
- model: CPA* (scGPT) on PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank
- model: CPA* (scGPT) on PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean
- model: CPA* (scGPT) on PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank
- model: CPA* (scGPT) on PerturBench CT-COSINE: covariate transfer on Srivatsan20, Cosine similarity of log fold change
- model: CPA* (scGPT) on PerturBench CT-COSINE-RANK: covariate transfer on Srivatsan20, Cosine LogFC rank
- model: CPA* (scGPT) on PerturBench CT-RMSE: covariate transfer on Srivatsan20, RMSE of the mean
- model: CPA* (scGPT) on PerturBench CT-RMSE-RANK: covariate transfer on Srivatsan20, RMSE mean rank