reported_finding: perturbation-ahlmann-20261011-protocol-replogle-k562-single
Reported finding of Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a)
None of the deep learning models was able to consistently outperform the mean prediction or the linear model (Fig. 2a and Extended Data Fig. 8). The paper states this for the single-perturbation benchmark as a whole (Adamson, Replogle K562 and Replogle RPE1), not for each dataset separately.
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 16 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 16 Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 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 deep learning models was able to consistently outperform the mean prediction or the linear model (Fig. 2a and Extended Data Fig. 8). The paper states this for the single-perturbation benchmark as a whole (Adamson, Replogle K562 and Replogle RPE1), not for each dataset separately. Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 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 16 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-replogle-k562-single Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 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-replogle-k562-single Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 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-replogle-k562-single-baselines-not-beaten
- field
- reported_finding
- value
- None of the deep learning models was able to consistently outperform the mean prediction or the linear model (Fig. 2a and Extended Data Fig. 8). The paper states this for the single-perturbation benchmark as a whole (Adamson, Replogle K562 and Replogle RPE1), not for each dataset separately.
- source locator
- Main text paragraph 16
- 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 after a scoping fix (see the dated review).