Evidence claim

intended_use: pheno-20261022-method-song2025-virtual-crispr-embedding-classifier

Evidence concern: excluded from comparisons

Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A"

Intended use of Virtual CRISPR embedding classifier

The authors present in silico prediction of CRISPR screen outcomes as a way to prioritise experiments and guide screen design, and propose using failures of LLM prediction on robust hits to flag novel biology. The evaluation itself is binary classification of pre-selected gene-phenotype queries; in vitro validation of predictions is listed as future work.

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6 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
Claimed field intended_use Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Abstract; Section 5; Limitations and Future Work Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	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.field` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Source location Abstract; Section 5; Limitations and Future Work Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Abstract; Section 5; Limitations and Future Work Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	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.source_locator` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Claimed value The authors present in silico prediction of CRISPR screen outcomes as a way to prioritise experiments and guide screen design, and propose using failures of LLM prediction on robust hits to flag novel biology. The evaluation itself is binary classification of pre-selected gene-phenotype queries; in vitro validation of predictions is listed as future work. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Abstract; Section 5; Limitations and Future Work Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	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.value` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Description Descriptive fact transcribed from the pinned source. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Abstract; Section 5; Limitations and Future Work Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	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: `description` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Relationship: subject pheno-20261022-method-song2025-virtual-crispr-embedding-classifier Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Abstract; Section 5; Limitations and Future Work Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	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: `links:subject:pheno-20261022-method-song2025-virtual-crispr-embedding-classifier` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Name intended_use: pheno-20261022-method-song2025-virtual-crispr-embedding-classifier Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Abstract; Section 5; Limitations and Future Work Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	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: `name` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact

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

1 source record and release history

Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Original source · Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30

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Stable ID: pheno-20261022-claim-song2025-stated-purpose

field: intended_use
value: The authors present in silico prediction of CRISPR screen outcomes as a way to prioritise experiments and guide screen design, and propose using failures of LLM prediction on robust hits to flag novel biology. The evaluation itself is binary classification of pre-selected gene-phenotype queries; in vitro validation of predictions is listed as future work.
source locator: Abstract; Section 5; Limitations and Future Work
review: method: source-hash-verification; ai-assisted-source-review; method note: Read the cited sections of the pinned PDF text layer and checked the claim's wording against them.; reviewer: claude; reviewer note: Claude (Opus 5.5) review agent, independent of the extractor; no human review claimed; date: 2026-10-22; artifact sha256: 6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c; retrieval url: https://aclanthology.org/2025.bionlp-1.30.pdf; note: Matches the Abstract ('thereby prioritizing experiments'), Section 5 and Limitations and Future Work ('Conduct in vitro validation').

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subject: Virtual CRISPR embedding classifier

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