Evidence claim

Relevance of Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) to "Assess models for genetic perturbation experiments"

Evidence concern: excluded from comparisons

The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A"

Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C"

The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods

The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C"

Panel A repeats Source Data Fig. 2 Panel A row for row (48, 262 and 413 rows per method over two splits). Methods restricts the analysis to 73, 398 and 629 perturbations predicted by all models; the sheet does not have these counts.Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Extended Data Fig. 8, sheet "Panel A"

Use case: Assess models for genetic perturbation experiments
Assessed by: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a)
Relevance: Proxy evidence
Endpoint: Mean L2 distance and mean Pearson delta between predicted and observed expression over the 1,000 most highly expressed genes, for scGPT, GEARS and three foundation models with a linear decoder (Geneformer, UCE, scBERT) against the mean baseline and a linear model, Replogle K562 (CRISPRi)

scGPT, GEARS and three foundation models with a linear decoder (Geneformer, UCE, scBERT) against the mean baseline and a linear model are scored on the same held-out perturbations, which is the methods-and-controls comparison the use case asks for, from authors who did not develop the deep models. It is proxy evidence: expression-prediction error does not establish mechanism or experimental prioritisation (use-case exclusion), and the per-model means are computed by Rewire from the authors' Source Data rather than printed.

7 reviewed evaluations

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.

60 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Citation locations 3 values source id perturbation-ahlmann-20261011-source-data-fig2 locator Source Data Fig. 2, sheet "Panel A" source id perturbation-ahlmann-20261011-source-data-ed-fig8 locator Source Data Extended Data Fig. 8, sheet "Panel A" source id perturbation-ahlmann-20261011-source-article locator Fig. 2 and Extended Data Fig. 8 legends; main text paragraphs 13 to 16; Methods Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.citation_locators` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Citation locations 3 values source id perturbation-ahlmann-20261011-source-data-fig2 locator Source Data Fig. 2, sheet "Panel A" source id perturbation-ahlmann-20261011-source-data-ed-fig8 locator Source Data Extended Data Fig. 8, sheet "Panel A" source id perturbation-ahlmann-20261011-source-article locator Fig. 2 and Extended Data Fig. 8 legends; main text paragraphs 13 to 16; Methods Context-only references	Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Extended Data Fig. 8 (41592_2025_2772_MOESM10_ESM.xlsx) as published with the article Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.citation_locators` Source artifact SHA-256: `040dae4985b4461817b96eb807390c8fe3f2c115523f4dfcb7692485aa0734e3` Hash scope: Hash scope not separately documented; inspect source record
Citation locations 3 values source id perturbation-ahlmann-20261011-source-data-fig2 locator Source Data Fig. 2, sheet "Panel A" source id perturbation-ahlmann-20261011-source-data-ed-fig8 locator Source Data Extended Data Fig. 8, sheet "Panel A" source id perturbation-ahlmann-20261011-source-article locator Fig. 2 and Extended Data Fig. 8 legends; main text paragraphs 13 to 16; Methods Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Fig. 2 (41592_2025_2772_MOESM4_ESM.xlsx) as published with the article Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.citation_locators` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Comparison group ahlmann2025-perturbation-baselines Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_group` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison group ahlmann2025-perturbation-baselines Context-only references	Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Extended Data Fig. 8 (41592_2025_2772_MOESM10_ESM.xlsx) as published with the article Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison_group` Source artifact SHA-256: `040dae4985b4461817b96eb807390c8fe3f2c115523f4dfcb7692485aa0734e3` Hash scope: Hash scope not separately documented; inspect source record
Comparison group ahlmann2025-perturbation-baselines Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Fig. 2 (41592_2025_2772_MOESM4_ESM.xlsx) as published with the article Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison_group` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Comparison title Deep learning and foundation models against simple baselines (Ahlmann-Eltze et al. 2025) Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_title` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison title Deep learning and foundation models against simple baselines (Ahlmann-Eltze et al. 2025) Context-only references	Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Extended Data Fig. 8 (41592_2025_2772_MOESM10_ESM.xlsx) as published with the article Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison_title` Source artifact SHA-256: `040dae4985b4461817b96eb807390c8fe3f2c115523f4dfcb7692485aa0734e3` Hash scope: Hash scope not separately documented; inspect source record
Comparison title Deep learning and foundation models against simple baselines (Ahlmann-Eltze et al. 2025) Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Fig. 2 (41592_2025_2772_MOESM4_ESM.xlsx) as published with the article Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison_title` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Constraints 3 values Inspect every linked evaluation's source locator, derivation and preserved limitations before citing a result. Do not combine these results with another protocol's results; splits, gene subsets and metric implementations differ between sources. Cite these values as computed by Rewire from the authors' Source Data, not as printed values. Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A" Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML Retrieved: 2026-10-11T00:48:13Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.constraints` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

How to read the evidence

Sources and history

Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

3 source records 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
Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Original source · Source Data Fig. 2 (41592_2025_2772_MOESM4_ESM.xlsx) as published with the article
Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Original source · Source Data Extended Data Fig. 8 (41592_2025_2772_MOESM10_ESM.xlsx) as published with the article

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Technical metadata and extraction receipts

Stable ID: use-case-mapping-perturbation-ahlmann-20261011-replogle-k562-single

field: links:assessed_by:perturbation-ahlmann-20261011-protocol-replogle-k562-single
value: perturbation-ahlmann-20261011-protocol-replogle-k562-single
relevance: proxy
endpoint: Mean L2 distance and mean Pearson delta between predicted and observed expression over the 1,000 most highly expressed genes, for scGPT, GEARS and three foundation models with a linear decoder (Geneformer, UCE, scBERT) against the mean baseline and a linear model, Replogle K562 (CRISPRi)
rationale: scGPT, GEARS and three foundation models with a linear decoder (Geneformer, UCE, scBERT) against the mean baseline and a linear model are scored on the same held-out perturbations, which is the methods-and-controls comparison the use case asks for, from authors who did not develop the deep models. It is proxy evidence: expression-prediction error does not establish mechanism or experimental prioritisation (use-case exclusion), and the per-model means are computed by Rewire from the authors' Source Data rather than printed.
constraints: Inspect every linked evaluation's source locator, derivation and preserved limitations before citing a result.; Do not combine these results with another protocol's results; splits, gene subsets and metric implementations differ between sources.; Cite these values as computed by Rewire from the authors' Source Data, not as printed values.
limitations: Values are computed by Rewire from the authors' per-perturbation Source Data (attributes.derivation on each result); the paper prints no per-model number.; The authors ran all models themselves; the deep models were not run by their developers, although the developers were asked to review the code.; No spread of the mean is stated, so small differences between models are not testable from these values.; The Fig. 2 legend's counts are split 1 only, and the Methods counts (73, 398, 629) match no sheet; the Source Data sources carry evidence concerns, so these results do not support automatic comparison.; scFoundation and CPA are not evaluated here; Fig. 2c (embedding variants, bootstrapped ratios) was not extracted because its aggregation is ambiguous.; UCE and scBERT post-perturbation embeddings use ground-truth expression of the perturbed genes; the authors note for UCE that this could leak test data (Methods 'Software versions and parameters').
citation locators: source id: perturbation-ahlmann-20261011-source-data-fig2; locator: Source Data Fig. 2, sheet "Panel A"; source id: perturbation-ahlmann-20261011-source-data-ed-fig8; locator: Source Data Extended Data Fig. 8, sheet "Panel A"; source id: perturbation-ahlmann-20261011-source-article; locator: Fig. 2 and Extended Data Fig. 8 legends; main text paragraphs 13 to 16; Methods
source locator: perturbation-ahlmann-20261011-source-article: Fig. 2 and Extended Data Fig. 8 legends and Methods; perturbation-ahlmann-20261011-source-data-fig2: sheet "Panel A"; perturbation-ahlmann-20261011-source-data-ed-fig8: sheet "Panel A"
revision: 1
reason: Recorded from the Ahlmann-Eltze et al. 2025 Source Data pass 2026-10-11 (data/omics/perturbation-ahlmann-derived-20261011); independently reviewed 2026-10-11.
comparison group: ahlmann2025-perturbation-baselines
comparison title: Deep learning and foundation models against simple baselines (Ahlmann-Eltze et al. 2025)
headline metric: l2-distance
stratum label: Replogle K562 (CRISPRi)
stratum order: 3
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: Checked the endpoint against the use case's question, inputs, output and exclusions; checked proxy against the relevance definitions; checked the limitations against the article and the Source Data evidence concerns; checked that every model on the protocol was scored on the same perturbations in each split. Checked 7 evaluations (listed in reviewed_evaluations); none excluded.; note: Proxy relevance confirmed in independent review. Pins are set by the integrator with use-cases:repin after the records are in the store. Not human scientific review. See docs/reviews/use-cases/genetic-perturbation-response-2026-10-11.md.
reviewed evaluations: perturbation-ahlmann-20261011-eval-replogle-k562-gears; perturbation-ahlmann-20261011-eval-replogle-k562-geneformer; perturbation-ahlmann-20261011-eval-replogle-k562-linear-model; perturbation-ahlmann-20261011-eval-replogle-k562-mean; perturbation-ahlmann-20261011-eval-replogle-k562-scbert; perturbation-ahlmann-20261011-eval-replogle-k562-scgpt; perturbation-ahlmann-20261011-eval-replogle-k562-uce
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subject: Assess models for genetic perturbation experiments

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