Perturbench cosine logfc of CPA* (noAdv) on Norman19 (PerturBench split)
0.55 ± 9 × 10 − 2 Perturbench cosine logfc
Methods
- Tested configuration
- CPA* (noAdv)
- Task
- PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change
- Dataset subset
- Norman19 (PerturBench split)
- Procedure
- Predict the effect of a pair of gene overexpressions from single perturbations, reported as the mean and one standard deviation over seeds.
- Evaluation
- CPA* (noAdv) on PerturBench CB-COSINE: combination prediction on Norman19, Cosine similarity of log fold change
- Coverage
- Not reported scored / Not reported eligible
- Uncertainty
- SD 0.09
- Evidence
- Author-reported evaluation · source checkedPerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis · Table 3, row(CPA ∗ (noAdv)), column(Cosine, log fold change (LogFC))
A source-checked result verifies the numerical transcription, not every model or protocol detail. Evaluation metadata: source checked. Source checked does not mean independently reproduced.
Reproduction
- Split
- Not reported
- Adaptation
- Not reported
- Scoring implementation
- Cosine similarity of log fold change
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.
Evidence
Source checking verifies the cited claim or transcription. It does not establish 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.
1 evidence row matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Reported result 0.55 ± 9 × 10 − 2 Individual claims | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Table 3, row(CPA ∗ (noAdv)), column(Cosine, log fold change (LogFC)) Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | source checked ["deterministic-table-parse"] · 2026-09-18 author reported Audit detailsSource checked, not reproduced. Metrics differ by task, so no composite score across tasks is computed or implied. Field: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. Extraction artifact SHA-256: |
Sources and history
Release 2026-11-02-a7f03c9e55a5 · Record review: source checked
1 source record 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-result-cpa-noadv-cb-cosine-cosine-logfc
- areas
- cells-tissues
- tasks
- combination prediction on Norman19, Cosine similarity of log fold change
- metric
- perturbench-cosine-logfc
- metric direction
- higher
- unit
- fraction
- printed value
- 0.55 ± 9 × 10 − 2
- numeric value
- 0.55
- uncertainty
- type: standard_deviation; value: 0.09
- source locator
- Table 3, row(CPA ∗ (noAdv)), column(Cosine, log fold change (LogFC))
- missing metadata
- denominator: reason: unextracted; seeds: reason: unreported
- review
- method: deterministic-table-parse; reviewer: codex; date: 2026-09-18; artifact sha256: 5c4804565dd9faa17a11853a79e9847dcb6da73715b4c91f62e5b274cc79f186; retrieval url: https://arxiv.org/html/2408.10609v1; method note: pinned HTML tables; row and column counts asserted; reviewer note: role: research agent; no human review claimed; note: Source checked, not reproduced. Metrics differ by task, so no composite score across tasks is computed or implied.