Configuration

LA (scGPT)

Baseline implemented by the PerturBench authors.

Overview

Baseline implemented by the PerturBench authors.

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 · 8 evaluations · 8 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.

Evaluation setupMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
LA (scGPT) on PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change Configuration: LA (scGPT)Task: PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold changeDataset subset: Norman19 (PerturBench split) Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Author-reported evaluation · Evaluation metadata: source checked
0.77 ± 4 × 10 − 3 cosine_logfc Unit: fraction · Direction: higher	Uncertainty: type: standard deviation; value: 0.004 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(LA (scGPT)), column(Cosine, log fold change (LogFC)) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Configuration: LA (scGPT)Task: PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rankDataset subset: Norman19 (PerturBench split) Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Author-reported evaluation · Evaluation metadata: source checked
0.0085 ± 1 × 10 − 3 cosine_logfc_rank Unit: fraction · Direction: lower	Uncertainty: type: standard deviation; value: 0.001 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rank Configuration: LA (scGPT)Task: PerturBench CB-RMSE-RANK: combination prediction on Norman19, RMSE mean rankDataset subset: Norman19 (PerturBench split) Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Author-reported evaluation · Evaluation metadata: source checked
0.013 ± 2 × 10 − 3 rmse_mean_rank Unit: fraction · Direction: lower	Uncertainty: type: standard deviation; value: 0.002 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(LA (scGPT)), column(RMSE, mean rank) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the mean Configuration: LA (scGPT)Task: PerturBench CB-RMSE: combination prediction on Norman19, RMSE of the meanDataset subset: Norman19 (PerturBench split) Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Author-reported evaluation · Evaluation metadata: source checked
0.044 ± 4 × 10 − 4 rmse_mean Unit: error · Direction: lower	Uncertainty: type: standard deviation; value: 0.0004 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(LA (scGPT)), column(RMSE, mean) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CT-COSINE: covariate transfer on Srivatsan20, Cosine similarity of log fold change Configuration: LA (scGPT)Task: PerturBench CT-COSINE: covariate transfer on Srivatsan20, Cosine similarity of log fold changeDataset subset: Srivatsan20 (PerturBench split) 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. Author-reported evaluation · Evaluation metadata: source checked
0.50 ± 4 × 10 − 3 cosine_logfc Unit: fraction · Direction: higher	Uncertainty: type: standard deviation; value: 0.004 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(LA (scGPT)), column(Cosine, log fold change (LogFC)) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CT-COSINE-RANK: covariate transfer on Srivatsan20, Cosine LogFC rank Configuration: LA (scGPT)Task: PerturBench CT-COSINE-RANK: covariate transfer on Srivatsan20, Cosine LogFC rankDataset subset: Srivatsan20 (PerturBench split) 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. Author-reported evaluation · Evaluation metadata: source checked
0.13 ± 7 × 10 − 3 cosine_logfc_rank Unit: fraction · Direction: lower	Uncertainty: type: standard deviation; value: 0.007 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(LA (scGPT)), column(Cosine, LogFC rank) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CT-RMSE-RANK: covariate transfer on Srivatsan20, RMSE mean rank Configuration: LA (scGPT)Task: PerturBench CT-RMSE-RANK: covariate transfer on Srivatsan20, RMSE mean rankDataset subset: Srivatsan20 (PerturBench split) 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. Author-reported evaluation · Evaluation metadata: source checked
0.14 ± 5 × 10 − 3 rmse_mean_rank Unit: fraction · Direction: lower	Uncertainty: type: standard deviation; value: 0.005 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(LA (scGPT)), column(RMSE, mean rank) Source checking is not independent reproduction.
LA (scGPT) on PerturBench CT-RMSE: covariate transfer on Srivatsan20, RMSE of the mean Configuration: LA (scGPT)Task: PerturBench CT-RMSE: covariate transfer on Srivatsan20, RMSE of the meanDataset subset: Srivatsan20 (PerturBench split) 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. Author-reported evaluation · Evaluation metadata: source checked
0.017 ± 1 × 10 − 4 rmse_mean Unit: error · Direction: lower	Uncertainty: type: standard deviation; value: 0.0001 Scored: Not reported · Eligible: Not reported	source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 2, row(LA (scGPT)), column(RMSE, mean) Source checking is not independent reproduction.

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.

0 evidence rows matching the loaded filters