Evidence claim
Relevance of Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4 to "Select perturbations for a defined cellular response"
- Use case
- Select perturbations for a defined cellular response
- Assessed by
- Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4
- Relevance
- Proxy evidence
- Endpoint
- Number of each screen's 200 strongest measured gene pairs that fall in the top 200 of the measured-plus-predicted ranking after four rounds of active learning (and by round, ensemble size and UCB weight), on four fully measured two-gene CRISPR viability screens (Norman, Simpson, Horlbeck K562, Horlbeck Jurkat), for NAIAD, RECOVER and GEARS under uniform, maximum-predicted-effect, UCB, uncertainty and mean-UCB acquisition
The only stored evidence on choosing which gene combinations to test: acquisition strategies compared at a fixed number of measured pairs from a shared seed set. Proxy, because the count includes strong pairs the model only predicted, the truth is the 200 largest viability effects in the same screen, and the screens are replayed rather than tested.
120 reviewed evaluations
- NAIAD with MPE acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with MPE acquisition on Horlbeck K562 (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with MPE acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with MPE acquisition on Simpson (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with UCB acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with UCB acquisition on Horlbeck K562 (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with UCB acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with UCB acquisition on Simpson (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uncertainty acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uncertainty acquisition on Horlbeck K562 (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uncertainty acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uncertainty acquisition on Simpson (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uniform acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uniform acquisition on Horlbeck K562 (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uniform acquisition on Norman (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with Uniform acquisition on Simpson (Tables 2 and 3, acquisition functions with NAIAD)
- NAIAD with MPE acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with MPE acquisition on Horlbeck K562 (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with MPE acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with MPE acquisition on Simpson (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with UCB acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with UCB acquisition on Horlbeck K562 (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with UCB acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with UCB acquisition on Simpson (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with Uniform acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with Uniform acquisition on Horlbeck K562 (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with Uniform acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with Uniform acquisition on Simpson (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with MPE acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with MPE acquisition on Horlbeck K562 (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with MPE acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with MPE acquisition on Simpson (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with UCB acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with UCB acquisition on Horlbeck K562 (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with UCB acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with UCB acquisition on Simpson (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with Uniform acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with Uniform acquisition on Horlbeck K562 (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with Uniform acquisition on Norman (Table 4, NAIAD against RECOVER by acquisition function)
- RECOVER (gene adaptation) with Uniform acquisition on Simpson (Table 4, NAIAD against RECOVER by acquisition function)
- NAIAD with MPE acquisition, ensemble of 1 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 1 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 1 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 1 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 3 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 3 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 3 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 3 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 5 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 5 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 5 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 5 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 7 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 7 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 7 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with MPE acquisition, ensemble of 7 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 1 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 1 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 1 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 1 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 3 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 3 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 3 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 3 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 5 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 5 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 5 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 5 on Simpson (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 7 on Horlbeck Jurkat (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 7 on Horlbeck K562 (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 7 on Norman (Table 5, NAIAD ensemble size)
- NAIAD with Uniform acquisition, ensemble of 7 on Simpson (Table 5, NAIAD ensemble size)
- GEARS with MPE acquisition on Norman (Table 7, NAIAD, RECOVER and GEARS over rounds on Norman)
- NAIAD with MPE acquisition on Norman (Table 7, NAIAD, RECOVER and GEARS over rounds on Norman)
- NAIAD with Uniform acquisition on Norman (Table 7, NAIAD, RECOVER and GEARS over rounds on Norman)
- RECOVER (gene adaptation) with MPE acquisition on Norman (Table 7, NAIAD, RECOVER and GEARS over rounds on Norman)
- NAIAD with Mean-UCB acquisition, beta = 0.25 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.25 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.25 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.25 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.5 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.5 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.5 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.5 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.75 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.75 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.75 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0.75 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 0 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1.5 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1.5 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1.5 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1.5 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 1 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 10 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 10 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 10 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 10 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 2 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 2 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 2 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 2 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 25 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 25 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 25 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 25 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 5 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 5 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 5 on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Mean-UCB acquisition, beta = 5 on Simpson (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Uniform acquisition on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Uniform acquisition on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Uniform acquisition on Norman (Table 8, Mean-UCB beta with NAIAD)
- NAIAD with Uniform acquisition on Simpson (Table 8, Mean-UCB beta with NAIAD)
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.
16 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
Citation locations1 values
| Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
Constraints5 values
| Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
| Endpoint Number of each screen's 200 strongest measured gene pairs that fall in the top 200 of the measured-plus-predicted ranking after four rounds of active learning (and by round, ensemble size and UCB weight), on four fully measured two-gene CRISPR viability screens (Norman, Simpson, Horlbeck K562, Horlbeck Jurkat), for NAIAD, RECOVER and GEARS under uniform, maximum-predicted-effect, UCB, uncertainty and mean-UCB acquisition Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 links:assessed_by:pheno-20261022-qin2025-protocol-al-top200 Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
| Headline metric true-positive-count Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
Limitations7 values
| Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 records275 values
| Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
| Rationale The only stored evidence on choosing which gene combinations to test: acquisition strategies compared at a fixed number of measured pairs from a shared seed set. Proxy, because the count includes strong pairs the model only predicted, the truth is the 200 largest viability effects in the same screen, and the screens are replayed rather than tested. The use case's setting says combinations need their own protocol, and this is one. The uniform arm is NAIAD (or RECOVER) trained on randomly chosen pairs and still ranks every pair with the model, so it is a random-acquisition control, not the yield of testing randomly chosen pairs that the collection plan's random baseline would give. Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 Add relevance judgement for a protocol extracted in the 2026-10-22 refresh (issue #184). 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) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
| Relevance proxy Context-only references | Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D 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 |
Sources and history
Release 2026-10-23-5347695500ed · Record review: source checked
1 source record and release history
- 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-mapping-phenotype-20261022-qin2025-al-top200
- field
- links:assessed_by:pheno-20261022-qin2025-protocol-al-top200
- value
- pheno-20261022-qin2025-protocol-al-top200
- relevance
- proxy
- endpoint
- Number of each screen's 200 strongest measured gene pairs that fall in the top 200 of the measured-plus-predicted ranking after four rounds of active learning (and by round, ensemble size and UCB weight), on four fully measured two-gene CRISPR viability screens (Norman, Simpson, Horlbeck K562, Horlbeck Jurkat), for NAIAD, RECOVER and GEARS under uniform, maximum-predicted-effect, UCB, uncertainty and mean-UCB acquisition
- rationale
- The only stored evidence on choosing which gene combinations to test: acquisition strategies compared at a fixed number of measured pairs from a shared seed set. Proxy, because the count includes strong pairs the model only predicted, the truth is the 200 largest viability effects in the same screen, and the screens are replayed rather than tested. The use case's setting says combinations need their own protocol, and this is one. The uniform arm is NAIAD (or RECOVER) trained on randomly chosen pairs and still ranks every pair with the model, so it is a random-acquisition control, not the yield of testing randomly chosen pairs that the collection plan's random baseline would give.
- constraints
- Cite Table 4 (three replicates with standard error) for round-4 comparisons. Table 2 and Table 3 are separate runs with no stated run count and differ from Table 4 by more than rounding.; Compare only within one screen; the budget and the share of pairs in the top 200 differ by screen.; Do not compare this count with AssayBench-Loop's fraction of hits found or with any count of tested hits.; Do not treat the Uniform rows as the use case's random-selection baseline.; Inspect every linked evaluation's source locator and limitations before citing a result.
- limitations
- Retrospective replay over already-measured screens; no new experiment.; A pair counts as found when it is in the top 200 of the measured-plus-predicted ranking, whether or not it was measured.; Viability phenotype only, with no significance threshold. Appendix D ranks the 'top P values' without saying whether by sign or magnitude, while MPE acquires the pairs with the highest absolute predicted effect (Appendix C).; Budgets differ: 500 measured pairs of 6,328 for Norman, 2,500 of 95,703 to 147,658 for the other screens.; The model is given every gene's single-gene effect, so combinations involving a gene with an unmeasured single effect are not covered.; Author-reported: NAIAD is the authors' method, and they adapted and ran RECOVER (from drugs to genes) and GEARS (on synthetic expression input).; At most three cross-fold replicates, which the paper does not define for active learning; Table 7 and Table 4 disagree by 0.4 on one Norman cell.
- revision
- 1
- reason
- Add relevance judgement for a protocol extracted in the 2026-10-22 refresh (issue #184). 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.
- citation locators
- source id: pheno-20261022-qin2025-source-pmlr; locator: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D
- source locator
- pheno-20261022-qin2025-source-pmlr: Sections 4.3 and 5.3; Table 2; Table 3; Table 4; Tables 5, 7 and 8; Appendices C and D
- headline metric
- true-positive-count
- reviewed evaluations
- pheno-20261022-qin2025-eval-table2-3-naiad-mpe-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table2-3-naiad-mpe-horlbeck2018-k562; pheno-20261022-qin2025-eval-table2-3-naiad-mpe-norman2019; pheno-20261022-qin2025-eval-table2-3-naiad-mpe-simpson2023; pheno-20261022-qin2025-eval-table2-3-naiad-ucb-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table2-3-naiad-ucb-horlbeck2018-k562; pheno-20261022-qin2025-eval-table2-3-naiad-ucb-norman2019; pheno-20261022-qin2025-eval-table2-3-naiad-ucb-simpson2023; pheno-20261022-qin2025-eval-table2-3-naiad-uncertainty-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table2-3-naiad-uncertainty-horlbeck2018-k562; pheno-20261022-qin2025-eval-table2-3-naiad-uncertainty-norman2019; pheno-20261022-qin2025-eval-table2-3-naiad-uncertainty-simpson2023; pheno-20261022-qin2025-eval-table2-3-naiad-uniform-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table2-3-naiad-uniform-horlbeck2018-k562; pheno-20261022-qin2025-eval-table2-3-naiad-uniform-norman2019; pheno-20261022-qin2025-eval-table2-3-naiad-uniform-simpson2023; pheno-20261022-qin2025-eval-table4-naiad-mpe-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table4-naiad-mpe-horlbeck2018-k562; pheno-20261022-qin2025-eval-table4-naiad-mpe-norman2019; pheno-20261022-qin2025-eval-table4-naiad-mpe-simpson2023; pheno-20261022-qin2025-eval-table4-naiad-ucb-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table4-naiad-ucb-horlbeck2018-k562; pheno-20261022-qin2025-eval-table4-naiad-ucb-norman2019; pheno-20261022-qin2025-eval-table4-naiad-ucb-simpson2023; pheno-20261022-qin2025-eval-table4-naiad-uniform-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table4-naiad-uniform-horlbeck2018-k562; pheno-20261022-qin2025-eval-table4-naiad-uniform-norman2019; pheno-20261022-qin2025-eval-table4-naiad-uniform-simpson2023; pheno-20261022-qin2025-eval-table4-recover-mpe-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table4-recover-mpe-horlbeck2018-k562; pheno-20261022-qin2025-eval-table4-recover-mpe-norman2019; pheno-20261022-qin2025-eval-table4-recover-mpe-simpson2023; pheno-20261022-qin2025-eval-table4-recover-ucb-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table4-recover-ucb-horlbeck2018-k562; pheno-20261022-qin2025-eval-table4-recover-ucb-norman2019; pheno-20261022-qin2025-eval-table4-recover-ucb-simpson2023; pheno-20261022-qin2025-eval-table4-recover-uniform-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table4-recover-uniform-horlbeck2018-k562; pheno-20261022-qin2025-eval-table4-recover-uniform-norman2019; pheno-20261022-qin2025-eval-table4-recover-uniform-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-1-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-1-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-1-norman2019; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-1-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-3-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-3-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-3-norman2019; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-3-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-5-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-5-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-5-norman2019; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-5-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-7-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-7-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-7-norman2019; pheno-20261022-qin2025-eval-table5-naiad-mpe-ensemble-7-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-1-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-1-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-1-norman2019; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-1-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-3-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-3-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-3-norman2019; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-3-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-5-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-5-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-5-norman2019; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-5-simpson2023; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-7-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-7-horlbeck2018-k562; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-7-norman2019; pheno-20261022-qin2025-eval-table5-naiad-uniform-ensemble-7-simpson2023; pheno-20261022-qin2025-eval-table7-gears-mpe-norman2019; pheno-20261022-qin2025-eval-table7-naiad-mpe-norman2019; pheno-20261022-qin2025-eval-table7-naiad-uniform-norman2019; pheno-20261022-qin2025-eval-table7-recover-mpe-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-25-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-25-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-25-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-25-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-5-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-5-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-5-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-5-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-75-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-75-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-75-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-75-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-0-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-5-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-5-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-5-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-5-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-1-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-10-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-10-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-10-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-10-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-2-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-2-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-2-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-2-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-25-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-25-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-25-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-25-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-5-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-5-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-5-norman2019; pheno-20261022-qin2025-eval-table8-naiad-mean-ucb-beta-5-simpson2023; pheno-20261022-qin2025-eval-table8-naiad-uniform-horlbeck2018-jurkat; pheno-20261022-qin2025-eval-table8-naiad-uniform-horlbeck2018-k562; pheno-20261022-qin2025-eval-table8-naiad-uniform-norman2019; pheno-20261022-qin2025-eval-table8-naiad-uniform-simpson2023
- review
- method: source-hash-verification; scope-review; ai-assisted-source-review; 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; reviewed at: 2026-10-22T18:00:00Z; note: Proxy confirmed: combination selection at a fixed number of measured pairs from a shared uniform seed set, but the count includes predicted pairs (Appendix D), the truth is the 200 strongest measured effects in the same screen, and the replay is retrospective. Uniform accepted as a random-acquisition control inside the model's pipeline (Section 5.3; Appendix C.5), not the use case's random-selection baseline. All 120 evaluations kept: Table 2/3 runs have null aggregation, which blocks automatic comparison with Table 4, and the one GEARS row carries its synthetic-input caveat. Reviewer corrected the 'strongest by magnitude' limitation, which Appendix D does not state. See docs/reviews/use-cases/phenotype-perturbation-selection-2026-10-22.md.
- pins
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