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NAIAD, trained once on a downsampled set

NAIAD trained once on a subsample of measured pairs to predict the viability of held-out pairs.

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Evaluations and results

1 evaluation · 12 results. Different protocols are not a single leaderboard.

Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency

Sorted by RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) (lower 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	RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)	RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)	RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
NAIAD, trained once on a downsampled setEvaluation	Simpson et al. 2023 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)	2.0 (0.2) (best in column)	1.9 (0.2) (best in column)	2.2 (0.1) (best in column)
NAIAD, trained once on a downsampled setEvaluation	Horlbeck et al. 2018 combinatorial CRISPRi in Jurkat T cells, gene-level viability (as processed by Qin et al. 2025)	3.0 (0.8)	2.8 (0.6)	3.0 (0.6)
NAIAD, trained once on a downsampled setEvaluation	Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025)	4.7 (1.2)	4.7 (0.1)	5.1 (1.8)
NAIAD, trained once on a downsampled setEvaluation	Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)	5.7 (0.8)	5.4 (0.6)	6.1 (1.9)

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
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 checked Methods, coverage and source NAIAD, 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"
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 checked Methods, coverage and source NAIAD, 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"
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 checked Methods, coverage and source NAIAD, 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"
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 K562, gene-level viability (as processed by Qin et al. 2025)	5.7 (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 checked Methods, coverage and source NAIAD, trained once on a downsampled set on Horlbeck K562 (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 K562"
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 K562, gene-level viability (as processed by Qin et al. 2025)	5.4 (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 checked Methods, coverage and source NAIAD, trained once on a downsampled set on Horlbeck K562 (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 K562"
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 K562, gene-level viability (as processed by Qin et al. 2025)	6.1 (1.9) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 1.9. 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, trained once on a downsampled set on Horlbeck K562 (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 K562"
Configuration: NAIAD, trained once on a downsampled set	Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025)	4.7 (1.2) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) unitless · lower Uncertainty: SE 1.2. 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 Norman (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 "Norman"
Configuration: NAIAD, trained once on a downsampled set	Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025)	4.7 (0.1) RMSE (test RMSE of pair viability, training set with each gene seen about 20 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 Norman (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 "Norman"
Configuration: NAIAD, trained once on a downsampled set	Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency Dataset: Norman et al. 2019 combinatorial CRISPRa in K562, gene-level viability (as processed by Qin et al. 2025)	5.1 (1.8) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average) unitless · lower Uncertainty: SE 1.8. 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 Norman (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 "Norman"
Configuration: NAIAD, trained once on a downsampled set	Protocol: 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 set	Protocol: 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 set	Protocol: 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"

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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-prediction

areas: cells-tissues
contexts: research
method types: supervised_machine_learning
reported name: NAIAD
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: Adaptive gene-embedding size by average gene frequency (Table 9); learning rate 0.01, batch size 1024, linear schedule with 10% warm-up, weight decay 0 (Appendix F).
source locator: Table 1 and Table 6, row 'NAIAD'; Appendix B
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.

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