Configuration

Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)

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"

Embedding classifier whose inputs are embeddings of GPT-4o-generated summaries of each screen component.

1 evaluation · 8 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

1 evaluation · 8 results. Different protocols are not a single leaderboard.

Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes · Dataset: Virtual CRISPR Difficult Benchmark

Sorted by AUPRC (Difficult Benchmark) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	AUPRC (Difficult Benchmark)	AUROC (Difficult Benchmark)	F1 (Difficult Benchmark)	False positive rate (Difficult Benchmark)	Negative predictive value (Difficult Benchmark)	Precision (Difficult Benchmark)	Recall (Difficult Benchmark)	Specificity (Difficult Benchmark)
Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)Evaluation	0.69	0.72	0.67	0.26	0.67	0.71	0.64	0.74

All 8 result rows with coverage, uncertainty and sources

Filter evaluations

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: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.69 AUPRC (Difficult Benchmark) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "AUPRC"
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.72 AUROC (Difficult Benchmark) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "AUROC"
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.67 F1 (Difficult Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "F1"
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.26 False positive rate (Difficult Benchmark) fraction · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "FPR"
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.67 Negative predictive value (Difficult Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "NPV"
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.71 Precision (Difficult Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "PPV"
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.64 Recall (Difficult Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "Sens."
Configuration: Virtual CRISPR embedding classifier, GPT-4o summary embeddings (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.74 Specificity (Difficult Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported 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 Summ. Emb. on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "Summ. Emb.", column "Spec."

Source checking is not independent reproduction. Release 2026-10-23-5347695500ed.

Use this configuration

How it works, versions and access

Additional methodological details, versions and access terms have not yet been extracted into a structured profile for this configuration. Consult the linked sources and evaluation records below.

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.

0 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

No evidence rows match these filters. Choose another scope or clear the search.

How to read the evidence

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

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: pheno-20261022-config-song2025-embedding-classifier-summary

areas: cells-tissues
contexts: research
method types: supervised_machine_learning
reported name: Summ. Emb.
configuration type: paper_evaluation
foundation model eligible: false
parameters: text-embedding-3-large embeddings (3,072 dimensions) of perturbation method, gene symbol, cell line and phenotype, concatenated to 12,288 dimensions; 5-layer MLP with hidden layers [6144, 3072, 1536, 768]; binary cross-entropy; AdamW lr 0.001, weight decay 0.01, batch 8192; LR reduced after 5 epochs without validation improvement; early stopping after 15; probabilities binarised with Youden's J statistic; component texts first summarised by GPT-4o (Appendix Table 6 prompt); trained for 3 epochs
training setting: Trained on 1,678 BioGRID-ORCS screens in human or mouse cells (release not stated), 5 screens held out for validation
score training overlap: Evaluated only on the Difficult Benchmark, which the authors state contains no BioGRID-ORCS data
access reuse: Code at https://github.com/czbiohub-chi/immune-llm-acl (not pinned)
source locator: Table 2, row "Summ. Emb."; Section 4.2; Appendix A.3 and Table 6
missing metadata: model version: reason: unreported; note: The GPT-4o snapshot used for summaries is not stated; version: reason: unreported; note: No release, commit or weights checksum printed

Related records

configuration of: Virtual CRISPR embedding classifier
uses model: text-embedding-3-large
uses model: GPT-4o
system: Summ. Emb. on the Virtual CRISPR Difficult Benchmark

Suggest a correction