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Dataset

Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)

Symmetric pairwise CRISPR interference screen in K562: 147,658 unique two-gene combinations of 543 genes, each scored as gene-level log-fold change in cell viability against negative controls.

Evaluation results

5 evaluations · 48 results. Different protocols are not a single leaderboard.

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Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configurationProtocol and datasetFindingEvidence and details
Configuration: GEARS, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		8.7 (10.3) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)
unitless · lower

Uncertainty: SE 10.3. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Independent external evaluation · Source checked
Methods, coverage and source

GEARS, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: GEARS, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		3.5 (2.0) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)
unitless · lower

Uncertainty: SE 2.0. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Independent external evaluation · Source checked
Methods, coverage and source

GEARS, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: GEARS, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		5.4 (3.4) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
unitless · lower

Uncertainty: SE 3.4. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Independent external evaluation · Source checked
Methods, coverage and source

GEARS, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: Linear regression on single-gene effects, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		3.3 (0.2) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)
unitless · lower

Uncertainty: SE 0.2. 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

Linear regression on single-gene effects, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: Linear regression on single-gene effects, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		3.3 (0.2) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)
unitless · lower

Uncertainty: SE 0.2. 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

Linear regression on single-gene effects, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: Linear regression on single-gene effects, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		3.3 (0.3) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
unitless · lower

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

Linear regression on single-gene effects, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: MLP on summed gene embeddings, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		2.4 (0.4) RMSE (test RMSE of pair viability, training set with each gene seen about 10 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 checked
Methods, coverage and source

MLP on summed gene embeddings, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: MLP on summed gene embeddings, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		2.0 (0.3) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)
unitless · lower

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

MLP on summed gene embeddings, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: MLP on summed gene embeddings, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		4.3 (0.9) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
unitless · lower

Uncertainty: SE 0.9. 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

MLP on summed gene embeddings, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: NAIAD, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		2.0 (0.2) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)
unitless · lower

Uncertainty: SE 0.2. 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, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: NAIAD, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		1.9 (0.2) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)
unitless · lower

Uncertainty: SE 0.2. 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, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: NAIAD, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		2.2 (0.1) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
unitless · lower

Uncertainty: SE 0.1. 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, trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: RECOVER (gene adaptation), trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		2.5 (0.2) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)
unitless · lower

Uncertainty: SE 0.2. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Independent external evaluation · Source checked
Methods, coverage and source

RECOVER (gene adaptation), trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: RECOVER (gene adaptation), trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		1.9 (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 checked
Methods, coverage and source

RECOVER (gene adaptation), trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: RECOVER (gene adaptation), trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)		3.9 (0.4) RMSE (test RMSE of pair viability, training set with each gene seen about 4 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 checked
Methods, coverage and source

RECOVER (gene adaptation), trained once on a downsampled set on Simpson (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 "Simpson"
Configuration: NAIAD with MPE acquisitionProtocol: 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)		27 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 checked
Methods, coverage and source

NAIAD with MPE acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with MPE acquisitionProtocol: 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 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 checked
Methods, coverage and source

NAIAD with MPE acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with UCB acquisitionProtocol: 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)		18.8 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 checked
Methods, coverage and source

NAIAD with UCB acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with UCB acquisitionProtocol: 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)		111 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 checked
Methods, coverage and source

NAIAD with UCB acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with Uncertainty acquisitionProtocol: 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)		9.5 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 checked
Methods, coverage and source

NAIAD with Uncertainty acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with Uncertainty acquisitionProtocol: 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)		74 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 checked
Methods, coverage and source

NAIAD with Uncertainty acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with Uniform acquisitionProtocol: 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)		7.5 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 checked
Methods, coverage and source

NAIAD with Uniform acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with Uniform acquisitionProtocol: 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)		66 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 checked
Methods, coverage and source

NAIAD with Uniform acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with MPE acquisitionProtocol: 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)		141.7 (3.7) 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 3.7. 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 MPE acquisition on Simpson (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 "Simpson"
Configuration: NAIAD with UCB acquisitionProtocol: 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)		102.7 (8.1) 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 8.1. 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 UCB acquisition on Simpson (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 "Simpson"

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

Research readiness

0 of 4 readiness checks met. These checks assess whether the evidence supports a reproducible investigation; a source-checked score alone does not meet them.

Readiness checks, gaps and artifacts

Release 2026-10-23-5347695500ed · Evidence verified: Not verified

Evidence incomplete

Replay metrics

Exact outcomes, predictions, identifiers and evaluator are connected.

Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
  • File checksums match the recorded files: not yet verified
  • Predictions are matched to the right samples: not yet verified
  • Score meaning and direction are confirmed: not yet verified
  • Metrics are recomputed from the saved predictions: not yet verified

Verified: Not verified

Evidence incomplete

Investigate discrepancies

Replay evidence includes annotations and an assessment of dependence. Unknown independence permits descriptive analysis only.

Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
  • File checksums match the recorded files: not yet verified
  • Predictions are matched to the right samples: not yet verified
  • Score meaning and direction are confirmed: not yet verified
  • Metrics are recomputed from the saved predictions: not yet verified
  • Sample annotations are recorded: not yet verified
  • Dependence between samples is assessed: not yet verified

Verified: Not verified

Evidence incomplete

Run locally

A pinned recipe describes the inputs, environment and resource requirements.

Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
  • File checksums match the recorded files: not yet verified
  • Predictions are matched to the right samples: not yet verified
  • Score meaning and direction are confirmed: not yet verified
  • A pinned run recipe exists: not yet verified
  • Compute requirements are estimated: not yet verified

Verified: Not verified

Evidence incomplete

Validate independently

Separate data and exposure records support an independent test.

Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
  • File checksums match the recorded files: not yet verified
  • Predictions are matched to the right samples: not yet verified
  • Score meaning and direction are confirmed: not yet verified
  • Independent validation data exist: not yet verified
  • Overlap with training data is checked: not yet verified

Verified: Not verified

Readiness describes the evidence in this release. Availability on your computer is checked separately when an investigation runs. Existing data exposure can prevent independent validation even when files are available.

Artifacts and reproduction

No verified artifact manifest is connected to this record yet. The gaps above identify what is needed before analysis can begin.

Dataset and evaluation context

A dataset supplies biological observations. The evaluation protocol defines how those observations are split, used and scored.

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.

11 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statementOriginal source and locationReview and provenance
Assay
Bulk combinatorial CRISPR interference screen; phenotype is the log-fold change in cell viability relative to negative-control treatments, averaged over all guide pairs targeting a gene pair
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.assay

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Context
K562
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.context

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Label semantics
Continuous viability effect per gene pair. Single-gene effects come from guide pairs of one targeting and one non-targeting guide.
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.label_semantics

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Population
147,658 unique gene combinations involving 543 genes
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.population

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Scope note
Used both as the candidate pool and as ground truth: the 200 strongest measured pairs in the whole screen are the targets of the active-learning protocol.
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.scope_note

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Source location
Appendix A.1; Section 4.2 (combination counts)
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.source_locator

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Species
Homo sapiens
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.species

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Total
147658
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.total

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Version
Gene-level viability (gamma) computed by Qin et al. 2025, Appendix A.1, from the published screen
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.version

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Description
Symmetric pairwise CRISPR interference screen in K562: 147,658 unique two-gene combinations of 543 genes, each scored as gene-level log-fold change in cell viability against negative controls.
Context-only references		Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Appendix A.1; Section 4.2 (combination counts)

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

Audit details

Field: description

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

How to read the evidence

Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release historyDownload this release (gzip)
Technical metadata and extraction receipts

Stable ID: pheno-20261022-qin2025-data-simpson2023-viability

areas
cells-tissues
contexts
research
version
Gene-level viability (gamma) computed by Qin et al. 2025, Appendix A.1, from the published screen
assay
Bulk combinatorial CRISPR interference screen; phenotype is the log-fold change in cell viability relative to negative-control treatments, averaged over all guide pairs targeting a gene pair
context
K562
species
Homo sapiens
population
147,658 unique gene combinations involving 543 genes
total
147658
label semantics
Continuous viability effect per gene pair. Single-gene effects come from guide pairs of one targeting and one non-targeting guide.
scope note
Used both as the candidate pool and as ground truth: the 200 strongest measured pairs in the whole screen are the targets of the active-learning protocol.
source locator
Appendix A.1; Section 4.2 (combination counts)
missing metadata
accession: reason: unreported; note: The paper cites the screens but prints no accession or processed-file location.
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.
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