Held-out evidence resolves follow-up measurement decisions in biological screens
cs.LG
Submitted: 2026-07-30
Updated: 2026-09-01
Code: https://github.com/JIABI/mard_autonomous
License: http://creativecommons.org/licenses/by/4.0/
The gist: Machine learning determines which follow-up measurements biological screens collect.
Terminology
Abstract
Machine learning determines which follow-up measurements biological screens collect. In a six-rule Cell Painting battery, the highest-value rule would re-image 96.01% of the library and had a 97.14% false-activation upper bound, showing why predicted value alone cannot justify replacing a fixed plan. We developed OPAL, a held-out decision test that freezes a rule and judges unnecessary measurement, coverage and value after cost against archive-specific criteria fixed before final evaluation. A development-selected sparse Cell Painting rule had 18.2-fold lower added-well burden, but its false-discovery bound exceeded 35%, so the fixed plan remained. LINCS--LJP favored broad acquisition under point-estimate criteria set during development, not selective saving. CTRP required fallback because its frozen score missed measured opportunity. OPAL separates optimization from evidence sufficient to change an experiment.
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