rewirebio.iobenchmarks
Evidence claim

Relevance of Norman19 dual perturbations (K562 CRISPRa) to "Assess models for genetic perturbation experiments"

Use case
Assess models for genetic perturbation experiments
Assessed by
PerturBench v4 combination prediction of held-out dual perturbations on Norman19
Relevance
Proxy evidence
Endpoint
Cosine similarity and rank of predicted log fold change, RMSE and RMSE rank of the mean expression profile, MMD and DEG recall for held-out K562 CRISPRa dual-gene perturbations (Norman19), for CPA, SAMS-VAE, Biolord, GEARS, Latent Additive (with and without scGPT embeddings), Decoder and Linear models

The protocol compares perturbation-response models and simple baselines on the same held-out dual perturbations, which is the methods-and-controls comparison the use case asks for. It is proxy evidence because the endpoint is expression agreement, not experimental prioritisation.

11 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 statementOriginal source and locationReview and provenance
Citation locations
1 values
  • source id
    lit-cells-networks-20261011-perturbench-v4-source-wu2025
    locator
    Tables 3 and 6; Section 4.2; Appendix D.4-D.5
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 3 and 6; Section 4.2; Appendix D.4-D.5

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 3 and 6; Section 4.2; Appendix D.4-D.5

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 3 and 6; Section 4.2; Appendix D.4-D.5

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; MMD, MMD PCA and DEG recall are not printed ('-') for the CPA* (scGPT), LA (scGPT) and GEARS rows, and 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 3 and 6; Section 4.2; Appendix D.4-D.5

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, RMSE and RMSE rank of the mean expression profile, MMD and DEG recall for held-out K562 CRISPRa dual-gene perturbations (Norman19), for CPA, SAMS-VAE, Biolord, GEARS, Latent Additive (with and without scGPT embeddings), Decoder and Linear models
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 3 and 6; Section 4.2; Appendix D.4-D.5

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-norman19
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 3 and 6; Section 4.2; Appendix D.4-D.5

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 3 and 6; Section 4.2; Appendix D.4-D.5

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
5 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).
  • GEARS rows have no MMD or DEG recall (printed '-').
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 3 and 6; Section 4.2; Appendix D.4-D.5

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

Pinned records
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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 3 and 6; Section 4.2; Appendix D.4-D.5

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.

Inspected artifact

Rationale
The protocol compares perturbation-response models and simple baselines on the same held-out dual perturbations, which is the methods-and-controls comparison the use case asks for. It is proxy evidence because the endpoint is expression agreement, not experimental prioritisation.
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 3 and 6; Section 4.2; Appendix D.4-D.5

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

How to read the evidence

Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release historyDownload this release (gzip)
Technical metadata and extraction receipts

Stable ID: use-case-mapping-lit-cells-networks-20261011-perturbench-v4-norman19

field
links:assessed_by:lit-cells-networks-20261011-perturbench-v4-protocol-norman19
value
lit-cells-networks-20261011-perturbench-v4-protocol-norman19
relevance
proxy
endpoint
Cosine similarity and rank of predicted log fold change, RMSE and RMSE rank of the mean expression profile, MMD and DEG recall for held-out K562 CRISPRa dual-gene perturbations (Norman19), for CPA, SAMS-VAE, Biolord, GEARS, Latent Additive (with and without scGPT embeddings), Decoder and Linear models
rationale
The protocol compares perturbation-response models and simple baselines on the same held-out dual perturbations, which is the methods-and-controls comparison the use case asks for. It is proxy evidence because the endpoint is expression agreement, not experimental prioritisation.
constraints
Inspect every linked evaluation's source locator before citing a result.; Compare models only within one table column; MMD, MMD PCA and DEG recall are not printed ('-') for the CPA* (scGPT), LA (scGPT) and GEARS rows, and 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).; GEARS rows have no MMD or DEG recall (printed '-').
citation locators
source id: lit-cells-networks-20261011-perturbench-v4-source-wu2025; locator: Tables 3 and 6; Section 4.2; Appendix D.4-D.5
source locator
lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3 and 6; Section 4.2; Appendix D.4-D.5
headline metric
perturbench-cosine-logfc
comparison group
perturbench-v4
comparison title
PerturBench v4 (Wu et al., NeurIPS 2025 Datasets and Benchmarks)
stratum label
Norman19 dual perturbations (K562 CRISPRa)
stratum order
1
reviewed evaluations
lit-cells-networks-20261011-perturbench-v4-eval-norman19-biolord; lit-cells-networks-20261011-perturbench-v4-eval-norman19-cpa; lit-cells-networks-20261011-perturbench-v4-eval-norman19-cpa-noadv; lit-cells-networks-20261011-perturbench-v4-eval-norman19-cpa-scgpt; lit-cells-networks-20261011-perturbench-v4-eval-norman19-decoder; lit-cells-networks-20261011-perturbench-v4-eval-norman19-gears-perturbench-norman19-run; lit-cells-networks-20261011-perturbench-v4-eval-norman19-la; lit-cells-networks-20261011-perturbench-v4-eval-norman19-la-scgpt; lit-cells-networks-20261011-perturbench-v4-eval-norman19-linear; lit-cells-networks-20261011-perturbench-v4-eval-norman19-sams-vae; lit-cells-networks-20261011-perturbench-v4-eval-norman19-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.
pins
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