Evidence claim
Evidence summary: Select perturbations for a defined cellular response
Summary of Select perturbations for a defined cellular response
The evidence comes from one prospective challenge and two retrospective replays, which measure different things. In the Cancer Immunotherapy Data Science Challenge, scored prospectively in mouse T cells, the 20 original Challenge 2 submissions had a top-k overlap AUC of 0.452 to 0.566, with 17 of the 20 between 0.50 and 0.57; a random ordering is expected to score 0.5. That score is not a ROC AUC, and the genes it scores were drawn from the leading Challenge 1 nominations. In the follow-up, two nominated targets exceeded the Screen 1 targets on the immune checkpoint blockade objective; the source reports 61 nominations, 57 selected targets, 59 screen-2 genes and 50 perturbations passing quality control without reconciling them, so no success fraction follows. AssayBench-Loop replays 20 genome-wide CRISPR screens published after 2021, with each policy choosing 10 rounds of 100 genes. One random draw found 4.9% of each screen's hits on average (enrichment 1.04 over the random expectation). Language models used directly as the policy, with hit labels, reached enrichment of 1.39 to 4.81, BPMF 4.49, and the authors' handoffs from a language model to their AssayFormer up to 5.67, with the Gemini-3.1-Pro handoff finding 27.7% of hits. Many recovered hits are common-essential genes: 30.1% for that handoff, 18.6% for the random draw and 99.1% for a baseline that picks earlier screens' hits. Test screens were kept only if the best language model already passed a performance floor on them, and the paper does not say whether the models saw the screens' publications in training. The authors ran every row, and no uncertainty is printed. For two-gene combinations, NAIAD's replay of four viability screens counted how many of each screen's 200 strongest pairs the model ranked in its top 200 after four rounds: 143.0, 141.7, 99.7 and 150.0 when it acquired the pairs with the largest predicted effect, against 93.3, 70.3, 38.7 and 81.3 when it acquired pairs uniformly at random (means of three replicates). That count includes pairs the model predicted but never measured, and the uniform arm still uses the model's ranking, so it is not the yield of testing random pairs. The challenge's submissions scored close to a random ordering, while in both replays the leading model-guided policies scored above the random or uniform arm; the two differ in endpoint, candidate set, setting and who ran them, so their results cannot be compared directly. No source compares a model shortlist with random selection prospectively at the same budget.
Plain-language summary of what the reviewed evidence shows. Draft until reviewed.
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.
36 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field summary Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read. | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py |
| Claimed field summary Context-only references | Cancer Immunotherapy Data Science Challenge ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 10.64898/2026.05.21.726863v1; 2026 preprint | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | CPPC Table S2 original and reimplemented submission comparisons ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Table S2 media-3.xlsx, 10.64898/2026.05.21.726863v1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record Archive member: media-3.xlsx |
| Claimed field summary Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
Pinned records902 values
| Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read. | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py |
Pinned records902 values
| Cancer Immunotherapy Data Science Challenge ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 10.64898/2026.05.21.726863v1; 2026 preprint | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Pinned records902 values
| CPPC Table S2 original and reimplemented submission comparisons ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Table S2 media-3.xlsx, 10.64898/2026.05.21.726863v1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record Archive member: media-3.xlsx |
Pinned records902 values
| Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Reason Revised in the 2026-10-22 refresh (issue #184) to add the AssayBench-Loop and NAIAD evidence; the CPPC text is kept as reviewed on 2026-10-10. Drafted by the Claude (Opus 5.5) judgement agent in the evidence-pass workflow for issue #184 (batch data/omics/use-case-refresh-phenotype-20261022); no human review claimed. Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read. | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py |
| Reason Revised in the 2026-10-22 refresh (issue #184) to add the AssayBench-Loop and NAIAD evidence; the CPPC text is kept as reviewed on 2026-10-10. Drafted by the Claude (Opus 5.5) judgement agent in the evidence-pass workflow for issue #184 (batch data/omics/use-case-refresh-phenotype-20261022); no human review claimed. Context-only references | Cancer Immunotherapy Data Science Challenge ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 10.64898/2026.05.21.726863v1; 2026 preprint | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-10-23-5347695500ed · Record review: source checked
4 source records and release history
- Cancer Immunotherapy Data Science Challenge · Original source · 10.64898/2026.05.21.726863v1; 2026 preprint
- CPPC Table S2 original and reimplemented submission comparisons · Original source · Table S2 media-3.xlsx, 10.64898/2026.05.21.726863v1
- Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens · Original source · arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed
- Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Original source · Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Technical metadata and extraction receipts
Stable ID: use-case-summary-phenotype-perturbation-selection
- field
- summary
- value
- The evidence comes from one prospective challenge and two retrospective replays, which measure different things. In the Cancer Immunotherapy Data Science Challenge, scored prospectively in mouse T cells, the 20 original Challenge 2 submissions had a top-k overlap AUC of 0.452 to 0.566, with 17 of the 20 between 0.50 and 0.57; a random ordering is expected to score 0.5. That score is not a ROC AUC, and the genes it scores were drawn from the leading Challenge 1 nominations. In the follow-up, two nominated targets exceeded the Screen 1 targets on the immune checkpoint blockade objective; the source reports 61 nominations, 57 selected targets, 59 screen-2 genes and 50 perturbations passing quality control without reconciling them, so no success fraction follows. AssayBench-Loop replays 20 genome-wide CRISPR screens published after 2021, with each policy choosing 10 rounds of 100 genes. One random draw found 4.9% of each screen's hits on average (enrichment 1.04 over the random expectation). Language models used directly as the policy, with hit labels, reached enrichment of 1.39 to 4.81, BPMF 4.49, and the authors' handoffs from a language model to their AssayFormer up to 5.67, with the Gemini-3.1-Pro handoff finding 27.7% of hits. Many recovered hits are common-essential genes: 30.1% for that handoff, 18.6% for the random draw and 99.1% for a baseline that picks earlier screens' hits. Test screens were kept only if the best language model already passed a performance floor on them, and the paper does not say whether the models saw the screens' publications in training. The authors ran every row, and no uncertainty is printed. For two-gene combinations, NAIAD's replay of four viability screens counted how many of each screen's 200 strongest pairs the model ranked in its top 200 after four rounds: 143.0, 141.7, 99.7 and 150.0 when it acquired the pairs with the largest predicted effect, against 93.3, 70.3, 38.7 and 81.3 when it acquired pairs uniformly at random (means of three replicates). That count includes pairs the model predicted but never measured, and the uniform arm still uses the model's ranking, so it is not the yield of testing random pairs. The challenge's submissions scored close to a random ordering, while in both replays the leading model-guided policies scored above the random or uniform arm; the two differ in endpoint, candidate set, setting and who ran them, so their results cannot be compared directly. No source compares a model shortlist with random selection prospectively at the same budget.
- source locator
- ucc-research-source-challenge-supp-s2: Supplementary Table S2, media-3.xlsx, sheet 'Challenge 1 & 2', column D rows 2 to 24; ucc-research-source-challenge-paper: Abstract and Results Figure 5; ucpheno-20261022-source-edwards2026: Table 1, rows 'Random', 'GPT-5.6 Sol', 'BPMF [41]', 'Gemini-3.1-Pro → AssayFormer', 'GPT-5.6 Sol → AssayFormer' and 'Prior hit baseline', columns 'EF', 'FH (%)' and '%ess (%)'; Methods 4.2 and Appendix A (test-screen criteria); Supplementary Table 4, Base LLMs group, column 'EF'; pheno-20261022-qin2025-source-pmlr: Table 4, rows 'NAIAD MPE' and 'NAIAD Uniform'
- revision
- 2
- reason
- Revised in the 2026-10-22 refresh (issue #184) to add the AssayBench-Loop and NAIAD evidence; the CPPC text is kept as reviewed on 2026-10-10. Drafted by the Claude (Opus 5.5) judgement agent in the evidence-pass workflow for issue #184 (batch data/omics/use-case-refresh-phenotype-20261022); no human review claimed.
- review
- method: independent-cell-check; ai-assisted-source-review; method note: Matched every outcome number with the printed_value of a source_checked result in the reviewed parts (AssayLoop random, base-LLM, BPMF, handoff and prior-hit rows; NAIAD Table 4 MPE and Uniform) and the CPPC range with the 20 stored Challenge 2 results (0.4517 to 0.5656, 17 between 0.50 and 0.57). Design figures (20 screens, 10 rounds of 100 genes, top 200, four rounds) checked against the reviewed protocol records. Checked that both cited new sources are source_checked with no evidence_concerns, that author-run comparisons and truth-set limits are named, and that nothing is recommended.; reviewer: claude; reviewer note: Claude (Opus 5.5) use-case review agent in the evidence-pass workflow for issue #184, independent of the agent that drafted these judgements and the summary; no human review claimed; the reviewer corrected the text; reviewed at: 2026-10-22T18:00:00Z; note: Revision 2 under the same ID. Reviewer resolved the 5.67 tie (Gemini-3.1-Pro and GPT-5.6 Sol handoffs) by naming the Gemini handoff the 27.7% and 30.1% belong to, added the best-LLM screen-selection floor and the unaddressed pre-training exposure, and replaced 'point in different directions' with an explicit, non-comparative description of the contrast. Descriptive only; no execution, human scientific review or clinical validation. See docs/reviews/use-cases/phenotype-perturbation-selection-2026-10-22.md.
- pins
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