LA (scGPT) on PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank
PerturBench evaluation of LA (scGPT) on combination prediction on Norman19, Cosine LogFC rank, scored with Cosine LogFC rank.
Methods and reproduction
PerturBench evaluation of LA (scGPT) on combination prediction on Norman19, Cosine LogFC rank, scored with Cosine LogFC rank.
- task
- PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank
- configuration
- LA (scGPT)
- dataset subset
- Norman19 (PerturBench split)
- Split
- Not reported
- Adaptation
- Not reported
- Scoring implementation
- Cosine LogFC rank
No execution recipe has been verified for this exact configuration and evaluation. A benchmark's general instructions may use different inputs, splits or model settings.
Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.
Evaluation procedure
Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds.
- Configuration
- LA (scGPT)
- Task
- PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank
- Dataset subset
- Norman19 (PerturBench split)
- origin
- Author-reported evaluation
- configuration
- Not reported
- protocol id
- perturbench-task-cb-cosine-rank
- metric implementation
- Cosine LogFC rank
Metadata review: source checked. Unreported conditions prevent automatic comparisons.
Evaluation results
Release 2026-09-17-134cd1815de8 · 1 evaluation · 1 metric row. 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 |
|---|---|---|
| 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. |
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.
10 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.comparison.metric_implementation Cosine LogFC rank Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| attributes.comparison.protocol_id perturbench-task-cb-cosine-rank Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| attributes.origin author_reported Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| attributes.protocol Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds. Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| attributes.source_locator Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| description PerturBench evaluation of LA (scGPT) on combination prediction on Norman19, Cosine LogFC rank, scored with Cosine LogFC rank. Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Relationship: benchmark perturbench-task-cb-cosine-rank Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Relationship: dataset perturbench-dataset-norman19 Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Relationship: model perturbench-method-la-scgpt Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| name LA (scGPT) on PerturBench CB-COSINE-RANK: combination prediction on Norman19, Cosine LogFC rank Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(LA (scGPT)), column(Cosine, LogFC rank) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
Sources and history
Release 2026-09-17-134cd1815de8 · 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-evaluation-la-scgpt-cb-cosine-rank
- areas
- cells-tissues
- tasks
- combination prediction on Norman19, Cosine LogFC rank
- origin
- author_reported
- protocol
- Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds.
- comparison
- protocol id: perturbench-task-cb-cosine-rank; metric implementation: Cosine LogFC rank
- missing metadata
- checkpoint revision: unreported; seeds: unreported; budget: unreported; split manifest: unextracted
- source locator
- Table 3, row(LA (scGPT)), column(Cosine, LogFC rank)