Evidence claim

training_data: 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"

Training data of Virtual CRISPR embedding classifier

Trained on 1,678 BioGRID-ORCS screens in human or mouse cells (from an initial pool of 1,924; about 22.6 million gene-level results; 7.74% hits under the high-confidence definition), with 5 screens held out for validation. The ORCS release used for training is not stated.

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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 training_data Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Section 3 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 Section 3 Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Section 3 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 Trained on 1,678 BioGRID-ORCS screens in human or mouse cells (from an initial pool of 1,924; about 22.6 million gene-level results; 7.74% hits under the high-confidence definition), with 5 screens held out for validation. The ORCS release used for training is not stated. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Section 3 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 ↗ Section 3 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 ↗ Section 3 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 training_data: pheno-20261022-method-song2025-virtual-crispr-embedding-classifier Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Section 3 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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Sources and history

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-training-data

field: training_data
value: Trained on 1,678 BioGRID-ORCS screens in human or mouse cells (from an initial pool of 1,924; about 22.6 million gene-level results; 7.74% hits under the high-confidence definition), with 5 screens held out for validation. The ORCS release used for training is not stated.
source locator: Section 3
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: Reviewer removed 'v1.1.16' and added the initial pool of 1,924: Section 3 does not state the training release.

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subject: Virtual CRISPR embedding classifier

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