← 研踪 PaperTrailarXiv:2611.15555 · v1
What Conformal Selection Can Certify in CRISPR Off-Target Design:Benchmark Leakage, Free Baselines, and a Finite-Sample Cohort Requirement
首次提交 2026-11-11 22:39
版本更新 2026-11-11 22:39
雷达观测 2026-11-13 11:11
摘要
Conformal selection promises distribution-free, finite-sample control of the error rate of a decision, and has been proposed as a safety layer for machine-learning CRISPR guide design. We ask whether such a certificate is attainable from the validated off-target data that exist. The constraint is arithmetic: a conformal p-value cannot fall below one divided by the calibration cohort size plus one, so a Benjamini-Hochberg selection certifies a single candidate only when that cohort is at least the candidate pool size divided by the tolerated error rate. Public cohorts supply ten validated guides, whereas a pool of two thousand at a ten percent error rate needs twenty thousand; the accepted set is consequently empty in over ninety-six percent of splits. Two benchmarking findings explain why this went unnoticed. The standard CIRCLE-seq file lists ten guides under eighty on-target strings, so grouping cross-validation on the sequence column trains and tests on one guide; corrected, pair-level discrimination and per-guide fidelity collapse. With that leakage removed, a training-free position-weight score and a bare count of near-match genomic sites are not beaten by a trained oracle. We give the corrected protocol, the baselines a new model must beat, and the cohort size a certified pipeline needs.
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-11 22:39 · 最新观测版本
What Conformal Selection Can Certify in CRISPR Off-Target Design:Benchmark Leakage, Free Baselines, and a Finite-Sample Cohort Requirement
Conformal selection promises distribution-free, finite-sample control of the error rate of a decision, and has been proposed as a safety layer for machine-learning CRISPR guide design. We ask whether such a certificate is attainable from the validated off-target data that exist. The constraint is arithmetic: a conformal p-value cannot fall below one divided by the calibration cohort size plus one, so a Benjamini-Hochberg selection certifies a single candidate only when that cohort is at least the candidate pool size divided by the tolerated error rate. Public cohorts supply ten validated guides, whereas a pool of two thousand at a ten percent error rate needs twenty thousand; the accepted set is consequently empty in over ninety-six percent of splits. Two benchmarking findings explain why this went unnoticed. The standard CIRCLE-seq file lists ten guides under eighty on-target strings, so grouping cross-validation on the sequence column trains and tests on one guide; corrected, pair-level discrimination and per-guide fidelity collapse. With that leakage removed, a training-free position-weight score and a bare count of near-match genomic sites are not beaten by a trained oracle. We give the corrected protocol, the baselines a new model must beat, and the cohort size a certified pipeline needs.
查看此版本解读 →查看此版本原文 ↗