Evidence claim

Relevance of Jiang24 held-out states (genetic perturbations) to "Assess models for genetic perturbation experiments"

Use case: Assess models for genetic perturbation experiments
Assessed by: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24
Relevance: Proxy evidence
Endpoint: Cosine similarity and rank of predicted log fold change, MMD, DEG recall and RMSE for held-out genetic perturbations in held-out cell line and cytokine states (Jiang24)

The protocol tests whether models trained on some cell states predict genetic perturbation responses in other states, which bears on using predictions in a new cell system. Proxy: expression agreement only, and the held-out state definition is inconsistent between the table caption and the appendix.

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.

19 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Citation locations 1 values source id lit-cells-networks-20261011-perturbench-v4-source-wu2025 locator Tables 4 and 11; Appendix D.4 'Jiang24 splits' Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.citation_locators` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Comparison group perturbench-v4 Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_group` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Comparison title PerturBench v4 (Wu et al., NeurIPS 2025 Datasets and Benchmarks) Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_title` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Constraints 3 values Inspect every linked evaluation's source locator before citing a result. Compare models only within one table column; DEG recall is not printed ('-') for the Decoder row. Do not combine these values with PerturBench v1 results (evidence-expansion-perturbench-5c480456): v4 reran the models and the numbers differ. Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.constraints` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Endpoint Cosine similarity and rank of predicted log fold change, MMD, DEG recall and RMSE for held-out genetic perturbations in held-out cell line and cytokine states (Jiang24) Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.endpoint` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Claimed field links:assessed_by:lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Headline metric perturbench-cosine-logfc Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.headline_metric` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Limitations 6 values The PerturBench authors trained and tuned every model themselves and developed the Linear, Latent Additive and Decoder baselines; CPA, SAMS-VAE, Biolord and GEARS are their reimplementations or wrappers. Preprint (arXiv v4 of the NeurIPS 2025 Datasets and Benchmarks paper); the proceedings version was not compared. Spread is one standard deviation over 5 training seeds of one HPO-selected configuration, not over data splits. Expression-prediction scores do not establish causal mechanism or experimental hit rates (use-case exclusion). Table 4 caption (9 of 30 states held out) and Appendix D.4 (12 cytokine-cell line combinations) describe the held-out states differently. The use case lists cross-cell-type transfer as outside the cited GEARS usage scope; this protocol tests transfer to held-out cell line and cytokine states and does not include GEARS, so it bears only on the use case's open question about performance in a new cell system. Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.limitations` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
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status attributes.artifact_sha256 sha256 bc98d4e6b3b80d990360172fedcd5e732535fb03f9d40ca0c85eb22d132e5aab record id use-case-genetic-perturbation-response fields status name description facets attributes.question attributes.decision attributes.inputs attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 440f0f2e30cb7c828212bed728de2420ba8e13521280be58be7bdcdfe57ac973 Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. 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Rationale The protocol tests whether models trained on some cell states predict genetic perturbation responses in other states, which bears on using predictions in a new cell system. Proxy: expression agreement only, and the held-out state definition is inconsistent between the table caption and the appendix. Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.rationale` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact

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PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Original source · arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version)

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Stable ID: use-case-mapping-lit-cells-networks-20261011-perturbench-v4-jiang24

field: links:assessed_by:lit-cells-networks-20261011-perturbench-v4-protocol-jiang24
value: lit-cells-networks-20261011-perturbench-v4-protocol-jiang24
relevance: proxy
endpoint: Cosine similarity and rank of predicted log fold change, MMD, DEG recall and RMSE for held-out genetic perturbations in held-out cell line and cytokine states (Jiang24)
rationale: The protocol tests whether models trained on some cell states predict genetic perturbation responses in other states, which bears on using predictions in a new cell system. Proxy: expression agreement only, and the held-out state definition is inconsistent between the table caption and the appendix.
constraints: Inspect every linked evaluation's source locator before citing a result.; Compare models only within one table column; DEG recall is not printed ('-') for the Decoder row.; Do not combine these values with PerturBench v1 results (evidence-expansion-perturbench-5c480456): v4 reran the models and the numbers differ.
limitations: The PerturBench authors trained and tuned every model themselves and developed the Linear, Latent Additive and Decoder baselines; CPA, SAMS-VAE, Biolord and GEARS are their reimplementations or wrappers.; Preprint (arXiv v4 of the NeurIPS 2025 Datasets and Benchmarks paper); the proceedings version was not compared.; Spread is one standard deviation over 5 training seeds of one HPO-selected configuration, not over data splits.; Expression-prediction scores do not establish causal mechanism or experimental hit rates (use-case exclusion).; Table 4 caption (9 of 30 states held out) and Appendix D.4 (12 cytokine-cell line combinations) describe the held-out states differently.; The use case lists cross-cell-type transfer as outside the cited GEARS usage scope; this protocol tests transfer to held-out cell line and cytokine states and does not include GEARS, so it bears only on the use case's open question about performance in a new cell system.
citation locators: source id: lit-cells-networks-20261011-perturbench-v4-source-wu2025; locator: Tables 4 and 11; Appendix D.4 'Jiang24 splits'
source locator: lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 4 and 11; Appendix D.4 'Jiang24 splits'
headline metric: perturbench-cosine-logfc
comparison group: perturbench-v4
comparison title: PerturBench v4 (Wu et al., NeurIPS 2025 Datasets and Benchmarks)
stratum label: Jiang24 held-out states (genetic perturbations)
stratum order: 2
reviewed evaluations: lit-cells-networks-20261011-perturbench-v4-eval-jiang24-cpa; lit-cells-networks-20261011-perturbench-v4-eval-jiang24-cpa-noadv; lit-cells-networks-20261011-perturbench-v4-eval-jiang24-decoder; lit-cells-networks-20261011-perturbench-v4-eval-jiang24-la; lit-cells-networks-20261011-perturbench-v4-eval-jiang24-linear; lit-cells-networks-20261011-perturbench-v4-eval-jiang24-sams-vae; lit-cells-networks-20261011-perturbench-v4-eval-jiang24-sams-vae-s
reason: Add a relevance judgement for a protocol from the 2026-10-11 cells-spatial and networks-mechanistic literature pass, reviewed in docs/reviews/2026-10-12-cells-networks-batches.md.
review: method: ai-assisted-source-review; scope-review; method note: Reviewer checked the endpoint against the use case's inputs, output and exclusions, the relevance rating and the limitations; every evaluation on the protocol had its values checked in the batch review. Corrections listed in review.md were applied by the extractor.; reviewer: claude; reviewer note: Claude review agent reviewer-cn-2, independent of the extractor lit-cells-networks; no human review claimed; reviewed at: 2026-10-12T08:44:00Z; note: Automated review; not human scientific review.
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subject: Assess models for genetic perturbation experiments

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