Evaluation

UCE on Replogle K562 (Ahlmann-Eltze et al. 2025)

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"

Benchmark run by the paper's authors. The per-model mean is computed by Rewire from the authors' Source Data rows; not reproduced.

Evaluation results

1 evaluation · 2 results. Different protocols are not a single leaderboard.

Protocol: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) · Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)

Sorted by L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits) (lower is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)	Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)
UCE (Ahlmann-Eltze et al. 2025)Evaluation	4.2Computed by Rewire from the authors' source data	0.409Computed by Rewire from the authors' source data

All 2 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: UCE (Ahlmann-Eltze et al. 2025)	Protocol: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	0.409 Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unitless · higher Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 262/262 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons 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" 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" Methods, coverage and source UCE on Replogle K562 (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-replogle-k562-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Source Data Extended Data Fig. 8 (MOESM10), sheet "Panel A", rows with dataset_name "replogle_k562_essential" and method "uce", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: UCE (Ahlmann-Eltze et al. 2025)	Protocol: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	4.2 L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unit-unreported · lower Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 262/262 perturbation-split rows scored	Independent external evaluation · Source checked 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" Methods, coverage and source UCE on Replogle K562 (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-replogle-k562-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2 (MOESM4), sheet "Panel A", rows with dataset_name "replogle_k562_essential" and method "uce", column "l2"; plotted as the red line in Fig. 2a

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

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Evaluation procedure

Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a)

Configuration: UCE (Ahlmann-Eltze et al. 2025)
Protocol: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a)
Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)

origin: Independent external evaluation
configuration: Primary source as retrieved 2026-10-11
protocol id: perturbation-ahlmann-20261011-protocol-replogle-k562-single
dataset version: GEARS distribution, Harvard Dataverse datafile 7458695
split: GEARS 'simulation' test-training split, run twice (two splits)
population: Unseen single perturbations in Source Data Fig. 2 Panel A over two splits (262 rows per model)
inputs: Expression of training perturbations and control; identity of the perturbed genes
adaptation: Fine-tuned, fitted or applied per split by the paper's authors with default parameters where possible
metric implementation: L2 distance and Pearson delta over the 1,000 most highly expressed genes in the control condition (Methods 'Evaluation metrics')
aggregation: Arithmetic mean per model over held-out perturbations, pooling test and val rows and all splits
budget: Not reported

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split: GEARS 'simulation' test-training split, run twice (two splits)
Adaptation: Fine-tuned, fitted or applied per split by the paper's authors with default parameters where possible
Scoring implementation: L2 distance and Pearson delta over the 1,000 most highly expressed genes in the control condition (Methods 'Evaluation metrics')

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Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.

Evidence

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Evidence table

Inspect claims, sources and review details

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38 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
Comparison: adaptation Fine-tuned, fitted or applied per split by the paper's authors with default parameters where possible Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: adaptation Fine-tuned, fitted or applied per split by the paper's authors with default parameters where possible Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Comparison: aggregation Arithmetic mean per model over held-out perturbations, pooling test and val rows and all splits Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: aggregation Arithmetic mean per model over held-out perturbations, pooling test and val rows and all splits Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Comparison: budget Not reported Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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	missing or unspecified No individual claim review recorded independent paper Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: budget Not reported Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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	missing or unspecified No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Comparison: dataset version GEARS distribution, Harvard Dataverse datafile 7458695 Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: dataset version GEARS distribution, Harvard Dataverse datafile 7458695 Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record
Comparison: inputs Expression of training perturbations and control; identity of the perturbed genes Context-only references	Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: inputs Expression of training perturbations and control; identity of the perturbed genes Context-only references	Source Data Fig. 2, Ahlmann-Eltze et al. 2025 Original source ↗ Fig. 2a and Extended Data Fig. 8a; Source Data method "uce" 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 independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `2ccfa7239c195f329e1632b51510adebfd493e0a5a7427a0f012e078e0e59c35` Hash scope: Hash scope not separately documented; inspect source record

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Sources and history

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

2 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

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Technical metadata and extraction receipts

Stable ID: perturbation-ahlmann-20261011-eval-replogle-k562-uce

areas: cells-tissues; single-cell
contexts: research
origin: independent_paper
protocol: perturbation-ahlmann-20261011-protocol-replogle-k562-single
version: Primary source as retrieved 2026-10-11
comparison: protocol id: perturbation-ahlmann-20261011-protocol-replogle-k562-single; dataset version: GEARS distribution, Harvard Dataverse datafile 7458695; split: GEARS 'simulation' test-training split, run twice (two splits); population: Unseen single perturbations in Source Data Fig. 2 Panel A over two splits (262 rows per model); inputs: Expression of training perturbations and control; identity of the perturbed genes; adaptation: Fine-tuned, fitted or applied per split by the paper's authors with default parameters where possible; metric implementation: L2 distance and Pearson delta over the 1,000 most highly expressed genes in the control condition (Methods 'Evaluation metrics'); aggregation: Arithmetic mean per model over held-out perturbations, pooling test and val rows and all splits; budget: Not reported
source locator: Fig. 2a and Extended Data Fig. 8a; Source Data method "uce"
limitations: Third-party model fine-tuned and run by the paper's authors, not by its developers.

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