reported_finding: perturbation-ahlmann-20261011-protocol-norman-double
Reported finding of Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a)
All models had a prediction error substantially higher than the additive baseline (Fig. 1a,b). Other summary statistics, including the Pearson delta and L2 distances over other gene subsets, gave the same overall result (Extended Data Fig. 2).
Descriptive fact transcribed from the pinned source.
Evidence
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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.
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6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field reported_finding Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 6 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 paragraph 6 Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 6 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 All models had a prediction error substantially higher than the additive baseline (Fig. 1a,b). Other summary statistics, including the Pearson delta and L2 distances over other gene subsets, gave the same overall result (Extended Data Fig. 2). Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 6 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 paragraph 6 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 paragraph 6 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 reported_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 paragraph 6 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-additive-not-beaten
- field
- reported_finding
- value
- All models had a prediction error substantially higher than the additive baseline (Fig. 1a,b). Other summary statistics, including the Pearson delta and L2 distances over other gene subsets, gave the same overall result (Extended Data Fig. 2).
- source locator
- Main text paragraph 6
- 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.