| Configuration: GEARS, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 24.2 (28.5) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 28.5. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGEARS, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "GEARS", column "Horlbeck Jurkat" |
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| Configuration: GEARS, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 20.7 (24.0) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 24.0. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGEARS, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "GEARS", column "Horlbeck Jurkat" |
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| Configuration: GEARS, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 13.5 (16.1) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 16.1. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGEARS, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "GEARS", column "Horlbeck Jurkat" |
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| Configuration: Linear regression on single-gene effects, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.8 (0.6) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 0.6. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLinear regression on single-gene effects, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "Linear", column "Horlbeck Jurkat" |
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| Configuration: Linear regression on single-gene effects, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.8 (0.6) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 0.6. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLinear regression on single-gene effects, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "Linear", column "Horlbeck Jurkat" |
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| Configuration: Linear regression on single-gene effects, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.9 (0.6) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 0.6. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLinear regression on single-gene effects, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "Linear", column "Horlbeck Jurkat" |
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| Configuration: MLP on summed gene embeddings, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.5 (1.0) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.0. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMLP on summed gene embeddings, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "MLP", column "Horlbeck Jurkat" |
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| Configuration: MLP on summed gene embeddings, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.0 (0.4) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 0.4. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMLP on summed gene embeddings, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "MLP", column "Horlbeck Jurkat" |
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| Configuration: MLP on summed gene embeddings, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 5.5 (2.1) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 2.1. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMLP on summed gene embeddings, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "MLP", column "Horlbeck Jurkat" |
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| Configuration: NAIAD, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.0 (0.8) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 0.8. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "NAIAD", column "Horlbeck Jurkat" |
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| Configuration: NAIAD, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 2.8 (0.6) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 0.6. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "NAIAD", column "Horlbeck Jurkat" |
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| Configuration: NAIAD, trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.0 (0.6) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 0.6. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD, trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "NAIAD", column "Horlbeck Jurkat" |
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| Configuration: RECOVER (gene adaptation), trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.7 (1.0) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.0. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRECOVER (gene adaptation), trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "RECOVER", column "Horlbeck Jurkat" |
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| Configuration: RECOVER (gene adaptation), trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 3.0 (0.4) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average) unitless · lower Uncertainty: SE 0.4. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRECOVER (gene adaptation), trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "RECOVER", column "Horlbeck Jurkat" |
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| Configuration: RECOVER (gene adaptation), trained once on a downsampled set | Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025) | 5.0 (0.7) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 0.7. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRECOVER (gene adaptation), trained once on a downsampled set on Horlbeck Jurkat (Tables 1 and 6, RMSE at fixed gene frequency) pheno-20261022-qin2025-protocol-downsampling-rmse Aggregation: Not reported Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "RECOVER", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with MPE acquisition | 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) | 23 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with MPE acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 3, row "MPE", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with MPE acquisition | 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) | 161 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: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with MPE acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 2, row "MPE", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with UCB acquisition | 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) | 12 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with UCB acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 3, row "UCB", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with UCB acquisition | 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) | 117 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: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with UCB acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 2, row "UCB", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with Uncertainty acquisition | 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) | 3.75 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uncertainty acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 3, row "Uncertainty", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with Uncertainty acquisition | 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) | 84 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: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uncertainty acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 2, row "Uncertainty", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with Uniform acquisition | 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) | 2.5 Count (marginal gain (P_4 - P_0)/4 in the screen's 200 strongest measured gene pairs found, round 0 to round 4) count · higher Uncertainty: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uniform acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 3, row "Uniform", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with Uniform acquisition | 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) | 79 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: Not reported by the source: The table prints no error and does not state the number of runs. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with Uniform acquisition on Horlbeck Jurkat (Tables 2 and 3, acquisition functions 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 2, row "Uniform", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with MPE acquisition | 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) | 150.0 (4.0) 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 4.0. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with MPE acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function) 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 4, row "NAIAD MPE", column "Horlbeck Jurkat" |
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| Configuration: NAIAD with UCB acquisition | 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) | 96.0 (8.5) 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 8.5. Caption: error is SE across three cross-fold replicates Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNAIAD with UCB acquisition on Horlbeck Jurkat (Table 4, NAIAD against RECOVER by acquisition function) 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 4, row "NAIAD UCB", column "Horlbeck Jurkat" |
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