The split of Norman19 that PerturBench evaluated on. The upstream dataset release is not catalogued here, so no claim is made that this matches its original splits.
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36 evaluations · 36 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: Biolord* | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.41 ± 2 × 10 − 2 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.02 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(Biolord ∗), column(Cosine, log fold change (LogFC)) |
| Configuration: Biolord* | Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Dataset subset: Norman19 (PerturBench split) | 0.027 ± 1 × 10 − 3 cosine_logfc_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.001 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceBiolord* 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(Biolord ∗), column(Cosine, LogFC rank) |
| Configuration: Biolord* | Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Dataset subset: Norman19 (PerturBench split) | 0.028 ± 1 × 10 − 3 rmse_mean_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.001 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceBiolord* 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(Biolord ∗), column(RMSE, mean rank) |
| Configuration: Biolord* | Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Dataset subset: Norman19 (PerturBench split) | 0.086 ± 6 × 10 − 4 rmse_mean error · lower Uncertainty: type: standard_deviation; value: 0.0006 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceBiolord* 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(Biolord ∗), column(RMSE, mean) |
| Configuration: CPA* | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.52 ± 6 × 10 − 2 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.06 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 ∗), column(Cosine, log fold change (LogFC)) |
| Configuration: CPA* | Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Dataset subset: Norman19 (PerturBench split) | 0.12 ± 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* 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 ∗), column(Cosine, LogFC rank) |
| Configuration: CPA* | Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Dataset subset: Norman19 (PerturBench split) | 0.17 ± 3 × 10 − 2 rmse_mean_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.03 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* 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 ∗), column(RMSE, mean rank) |
| Configuration: CPA* | Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Dataset subset: Norman19 (PerturBench split) | 0.079 ± 5 × 10 − 3 rmse_mean error · lower Uncertainty: type: standard_deviation; value: 0.005 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* 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 ∗), column(RMSE, mean) |
| Configuration: CPA* (noAdv) | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.55 ± 9 × 10 − 2 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.09 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 ∗ (noAdv)), column(Cosine, log fold change (LogFC)) |
| Configuration: CPA* (noAdv) | Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Dataset subset: Norman19 (PerturBench split) | 0.16 ± 4 × 10 − 2 cosine_logfc_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.04 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (noAdv) 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 ∗ (noAdv)), column(Cosine, LogFC rank) |
| Configuration: CPA* (noAdv) | Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Dataset subset: Norman19 (PerturBench split) | 0.18 ± 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* (noAdv) 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 ∗ (noAdv)), column(RMSE, mean rank) |
| Configuration: CPA* (noAdv) | Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Dataset subset: Norman19 (PerturBench split) | 0.073 ± 6 × 10 − 3 rmse_mean error · lower Uncertainty: type: standard_deviation; value: 0.006 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCPA* (noAdv) 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 ∗ (noAdv)), column(RMSE, mean) |
| 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: Decoder | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.73 ± 2 × 10 − 2 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.02 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(Decoder), column(Cosine, log fold change (LogFC)) |
| Configuration: Decoder | Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Dataset subset: Norman19 (PerturBench split) | 0.017 ± 6 × 10 − 3 cosine_logfc_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.006 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceDecoder 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(Decoder), column(Cosine, LogFC rank) |
| Configuration: Decoder | Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Dataset subset: Norman19 (PerturBench split) | 0.014 ± 4 × 10 − 4 rmse_mean_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.0004 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceDecoder 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(Decoder), column(RMSE, mean rank) |
| Configuration: Decoder | Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Dataset subset: Norman19 (PerturBench split) | 0.043 ± 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 sourceDecoder 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(Decoder), column(RMSE, mean) |
| Configuration: LA | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.79 ± 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(LA), column(Cosine, log fold change (LogFC)) |
| Configuration: LA | Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Dataset subset: Norman19 (PerturBench split) | 0.005 ± 2 × 10 − 3 cosine_logfc_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.002 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLA 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(LA), column(Cosine, LogFC rank) |
| Configuration: LA | Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Dataset subset: Norman19 (PerturBench split) | 0.014 ± 1 × 10 − 3 rmse_mean_rank fraction · lower Uncertainty: type: standard_deviation; value: 0.001 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLA 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(LA), column(RMSE, mean rank) |
| Configuration: LA | Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Dataset subset: Norman19 (PerturBench split) | 0.043 ± 4 × 10 − 4 rmse_mean error · lower Uncertainty: type: standard_deviation; value: 0.0004 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLA 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(LA), column(RMSE, mean) |
| Configuration: LA (scGPT) | Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Dataset subset: Norman19 (PerturBench split) | 0.77 ± 4 × 10 − 3 cosine_logfc fraction · higher Uncertainty: type: standard_deviation; value: 0.004 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(LA (scGPT)), column(Cosine, log fold change (LogFC)) |
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| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| description The split of Norman19 that PerturBench evaluated on. The upstream dataset release is not catalogued here, so no claim is made that this matches its original splits. Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis No field-specific location recorded Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| name Norman19 (PerturBench split) Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis No field-specific location recorded Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
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Release 2026-09-29-06401fd5b220 · Record review: source checked
Stable ID: perturbench-dataset-norman19