Dataset

Virtual CRISPR Simple Benchmark

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"

1,175 gene-phenotype queries from four BioGRID-ORCS screens, each labelled hit or no-hit.

Evaluation results

28 evaluations · 100 results. Different protocols are not a single leaderboard.

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.42 ± 0.13 F1 (Simple Benchmark) fraction · higher Uncertainty: SD 0.13. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-3.5-turbo (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-3.5-turbo", column "F1 Score"
Configuration: GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.39 ± 0.25 False positive rate (Simple Benchmark) fraction · lower Uncertainty: SD 0.25. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-3.5-turbo (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-3.5-turbo", column "FPR"
Configuration: GPT-4, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.44 ± 0.12 F1 (Simple Benchmark) fraction · higher Uncertainty: SD 0.12. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4 (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-4", column "F1 Score"
Configuration: GPT-4, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.38 ± 0.21 False positive rate (Simple Benchmark) fraction · lower Uncertainty: SD 0.21. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4 (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-4", column "FPR"
Configuration: GPT-4-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.32 ± 0.10 F1 (Simple Benchmark) fraction · higher Uncertainty: SD 0.10. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4-turbo (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-4-turbo", column "F1 Score"
Configuration: GPT-4-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.15 ± 0.08 False positive rate (Simple Benchmark) fraction · lower Uncertainty: SD 0.08. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4-turbo (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-4-turbo", column "FPR"
Configuration: GPT-4o, 0-shot, without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.48 F1 (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot N", column "F1"
Configuration: GPT-4o, 0-shot, without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.28 False positive rate (Simple Benchmark) fraction · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot N", column "FPR"
Configuration: GPT-4o, 0-shot, without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.65 Negative predictive value (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot N", column "NPV"
Configuration: GPT-4o, 0-shot, without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.53 Precision (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot N", column "PPV"
Configuration: GPT-4o, 0-shot, without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.44 Recall (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot N", column "Sensitivity"
Configuration: GPT-4o, 0-shot, without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.72 Specificity (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot N", column "Specificity"
Configuration: GPT-4o, 0-shot, with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.53 F1 (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot Y", column "F1"
Configuration: GPT-4o, 0-shot, with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.25 False positive rate (Simple Benchmark) fraction · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot Y", column "FPR"
Configuration: GPT-4o, 0-shot, with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.68 Negative predictive value (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot Y", column "NPV"
Configuration: GPT-4o, 0-shot, with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.58 Precision (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot Y", column "PPV"
Configuration: GPT-4o, 0-shot, with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.49 Recall (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot Y", column "Sensitivity"
Configuration: GPT-4o, 0-shot, with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.75 Specificity (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 0-shot, CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 0-shot Y", column "Specificity"
Configuration: GPT-4o, 1-shot (-), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.31 F1 (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) N", column "F1"
Configuration: GPT-4o, 1-shot (-), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.09 False positive rate (Simple Benchmark) fraction · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) N", column "FPR"
Configuration: GPT-4o, 1-shot (-), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.62 Negative predictive value (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) N", column "NPV"
Configuration: GPT-4o, 1-shot (-), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.62 Precision (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) N", column "PPV"
Configuration: GPT-4o, 1-shot (-), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.21 Recall (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) N", column "Sensitivity"
Configuration: GPT-4o, 1-shot (-), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.91 Specificity (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) N", column "Specificity"
Configuration: GPT-4o, 1-shot (-), with chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.46 F1 (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked 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" Methods, coverage and source GPT-4o, 1-shot (-), CoT Y, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (-) Y", column "F1"

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Release 2026-10-23-5347695500ed · Evidence verified: Not verified

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10 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
Assay CRISPR activation and knockout screens harmonised by BioGRID-ORCS Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.assay` 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
Label semantics A hit is a statistically significant effect in the hypothesised direction; a no-hit is a statistically significant effect in the opposite direction. Genes with no significant effect are not queried. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.label_semantics` 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
Population 1,175 gene-phenotype queries from four manually selected BioGRID-ORCS screens; 41.3% are hits Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.population` 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
Scope note Table 3 per screen (Genes, Hits): 1837 activation, primary CD4+ human T cells, increased TNF-alpha secretion (423, 140); 1885 activation, J774 macrophages, phagocytic inhibition (343, 139); 1835 activation, primary CD4+ human T cells, increased IL2 secretion (243, 76); 1733 knockout, HeLa cervical adenocarcinoma cells, increased RelA nuclear translocation (166, 130). Genes counts are after filtering to genes with a significant effect. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.scope_note` 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 Sections 2.1 to 2.3; Appendix Table 3 Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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
Species human and mouse cell models Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.species` 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
Total 1175 Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.total` 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
Version BioGRID-ORCS v1.1.16; screens 1837, 1885, 1835 and 1733 Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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.version` 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 1,175 gene-phenotype queries from four BioGRID-ORCS screens, each labelled hit or no-hit. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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
Name Virtual CRISPR Simple Benchmark Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Sections 2.1 to 2.3; Appendix Table 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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Release 2026-10-23-5347695500ed · Record review: source checked

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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-data-song2025-simple

areas: cells-tissues
contexts: research
version: BioGRID-ORCS v1.1.16; screens 1837, 1885, 1835 and 1733
total: 1175
species: human and mouse cell models
population: 1,175 gene-phenotype queries from four manually selected BioGRID-ORCS screens; 41.3% are hits
assay: CRISPR activation and knockout screens harmonised by BioGRID-ORCS
label semantics: A hit is a statistically significant effect in the hypothesised direction; a no-hit is a statistically significant effect in the opposite direction. Genes with no significant effect are not queried.
scope note: Table 3 per screen (Genes, Hits): 1837 activation, primary CD4+ human T cells, increased TNF-alpha secretion (423, 140); 1885 activation, J774 macrophages, phagocytic inhibition (343, 139); 1835 activation, primary CD4+ human T cells, increased IL2 secretion (243, 76); 1733 knockout, HeLa cervical adenocarcinoma cells, increased RelA nuclear translocation (166, 130). Genes counts are after filtering to genes with a significant effect.
source locator: Sections 2.1 to 2.3; Appendix Table 3
missing metadata: positives: reason: unreported; note: Only the 41.3% hit fraction and per-screen hit counts are printed, not a benchmark total. The Table 3 hit counts (140, 139, 76, 130) sum to 485 of 1,175, which matches 41.3%.

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