NAIAD with Mean-UCB acquisition, beta = 1.5; As run by Qin et al. 2025 (PMLR 267); no release or commit is printed

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Evaluations and results

1 evaluation · 4 results. Different protocols are not a single leaderboard.

Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4

Sorted by True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) (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	Protocol and dataset	True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning)
NAIAD with Mean-UCB acquisition, beta = 1.5Evaluation	Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025)	153.3 (4.6) (best in column)
NAIAD with Mean-UCB acquisition, beta = 1.5Evaluation	Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)	144.3 (2.3)
NAIAD with Mean-UCB acquisition, beta = 1.5Evaluation	Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025)	140.7 (0.3)
NAIAD with Mean-UCB acquisition, beta = 1.5Evaluation	Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)	97.7 (2.9)

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: NAIAD with Mean-UCB acquisition, beta = 1.5	Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4 Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025)	153.3 (4.6) True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: SE 4.6. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NAIAD with Mean-UCB acquisition, beta = 1.5 on Horlbeck Jurkat (Table 8, Mean-UCB beta with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 8, row "1.5", column "Horlbeck Jurkat"
Configuration: NAIAD with Mean-UCB acquisition, beta = 1.5	Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4 Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)	97.7 (2.9) True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: SE 2.9. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NAIAD with Mean-UCB acquisition, beta = 1.5 on Horlbeck K562 (Table 8, Mean-UCB beta with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 8, row "1.5", column "Horlbeck K562"
Configuration: NAIAD with Mean-UCB acquisition, beta = 1.5	Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4 Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025)	140.7 (0.3) True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: SE 0.3. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NAIAD with Mean-UCB acquisition, beta = 1.5 on Norman (Table 8, Mean-UCB beta with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 8, row "1.5", column "Norman"
Configuration: NAIAD with Mean-UCB acquisition, beta = 1.5	Protocol: Retrospective active learning on a fully measured two-gene CRISPR viability screen: top-200 gene pairs found by round 4 Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)	144.3 (2.3) True positives (the screen's 200 strongest measured gene pairs found among the 200 strongest measured-plus-predicted values, round 4 of active learning) count · higher Uncertainty: SE 2.3. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NAIAD with Mean-UCB acquisition, beta = 1.5 on Simpson (Table 8, Mean-UCB beta with NAIAD) pheno-20261022-qin2025-protocol-al-top200 Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 8, row "1.5", column "Simpson"

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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.

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Technical metadata and extraction receipts

Stable ID: pheno-20261022-qin2025-config-naiad-mean-ucb-beta-1-5

areas: cells-tissues
contexts: research
method types: supervised_machine_learning
reported name: NAIAD, Mean-UCB, β = 1.5
configuration type: paper_evaluation
foundation model eligible: false
version: As run by Qin et al. 2025 (PMLR 267); no release or commit is printed
access reuse: NAIAD code at https://github.com/NeptuneBio/NAIAD (Apache-2.0 per GitHub metadata); the commit used for the paper is not stated
parameters: NAIAD ensemble with adaptive gene-embedding size; seed set of uniformly sampled pairs shared by all acquisition functions.
source locator: Table 8, row '1.5'; Appendix C.5
provenance note: The paper's own method.
review: method: source-hash-verification; scope-review; reviewer: claude; reviewer note: Claude (Opus 5.5) review agent, independent of the extraction agent; no human review claimed; date: 2026-10-22; artifact sha256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb; retrieval url: https://raw.githubusercontent.com/mlresearch/v267/main/assets/qin25g/qin25g.pdf; method note: Re-hashed the PDF in the scratchpad (SHA-256 matches the source record). Read the cited sections in the pdftotext layer of the pinned PDF.; note: Fields checked against the cited locator; nothing found that the source does not state.
selection: Mean-UCB acquisition: score = alpha x ensemble mean + beta x ensemble standard deviation, beta = 1.5 (Appendix C.5); alpha for Table 8 is not stated

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