genetic_interaction_finding: perturbation-ahlmann-20261011-protocol-norman-double
Genetic interaction finding of Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a)
None of the models was better than the 'no change' baseline at predicting genetic interactions (Fig. 1c), and the same ranking was observed with other metrics (Extended Data Fig. 4). Fig. 1c and Extended Data Fig. 4 were not extracted.
Descriptive fact transcribed from the pinned source.
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.
6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field genetic_interaction_finding Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 8 and 9 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location Main text paragraphs 8 and 9 Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 8 and 9 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed value None of the models was better than the 'no change' baseline at predicting genetic interactions (Fig. 1c), and the same ranking was observed with other metrics (Extended Data Fig. 4). Fig. 1c and Extended Data Fig. 4 were not extracted. Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 8 and 9 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Description Descriptive fact transcribed from the pinned source. Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 8 and 9 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: subject perturbation-ahlmann-20261011-protocol-norman-double Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 8 and 9 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Name genetic_interaction_finding: perturbation-ahlmann-20261011-protocol-norman-double Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 8 and 9 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines · Original source · Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML
Technical metadata and extraction receipts
Stable ID: perturbation-ahlmann-20261011-claim-double-interactions-no-change
- field
- genetic_interaction_finding
- value
- None of the models was better than the 'no change' baseline at predicting genetic interactions (Fig. 1c), and the same ranking was observed with other metrics (Extended Data Fig. 4). Fig. 1c and Extended Data Fig. 4 were not extracted.
- source locator
- Main text paragraphs 8 and 9
- review
- method: source-hash-verification; ai-assisted-source-review; reviewer: claude; reviewer note: Separate Claude (Opus 5.5) review agent, independent of the extractor; no human review claimed; reviewed at: 2026-10-11T03:00:00Z; artifact sha256: dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12328236/fullTextXML; method note: Compared the claim with the cited paragraph of the re-downloaded article XML.; note: Hand transcription by the extractor. Independent review 2026-10-11: matches the source.