reported_finding: perturbation-ahlmann-20261011-method-linear-model
Reported finding of Linear perturbation model (Ahlmann-Eltze et al. 2025)
The linear model with gene embeddings from scFoundation or scGPT, or the perturbation embedding from GEARS, performed as well as or better than scGPT and GEARS with their own decoders, and the scFoundation and scGPT gene embeddings outperformed the mean baseline but not consistently the linear model fitted on the training data (Fig. 2c). A linear model with the perturbation embedding pretrained on Replogle data (K562 for Adamson and RPE1, RPE1 for K562) consistently outperformed all other models (paragraph 18 cites no figure for this). Fig. 2c was not extracted.
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 paragraphs 17 and 18 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 17 and 18 Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 17 and 18 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 The linear model with gene embeddings from scFoundation or scGPT, or the perturbation embedding from GEARS, performed as well as or better than scGPT and GEARS with their own decoders, and the scFoundation and scGPT gene embeddings outperformed the mean baseline but not consistently the linear model fitted on the training data (Fig. 2c). A linear model with the perturbation embedding pretrained on Replogle data (K562 for Adamson and RPE1, RPE1 for K562) consistently outperformed all other models (paragraph 18 cites no figure for this). Fig. 2c was not extracted. Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 17 and 18 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 17 and 18 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-method-linear-model Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 17 and 18 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-method-linear-model Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraphs 17 and 18 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-linear-model-pretrained-embeddings
- field
- reported_finding
- value
- The linear model with gene embeddings from scFoundation or scGPT, or the perturbation embedding from GEARS, performed as well as or better than scGPT and GEARS with their own decoders, and the scFoundation and scGPT gene embeddings outperformed the mean baseline but not consistently the linear model fitted on the training data (Fig. 2c). A linear model with the perturbation embedding pretrained on Replogle data (K562 for Adamson and RPE1, RPE1 for K562) consistently outperformed all other models (paragraph 18 cites no figure for this). Fig. 2c was not extracted.
- source locator
- Main text paragraphs 17 and 18
- 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).