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arXiv · 2605.10196

Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments

Andrea Rubbi·Arpit Merchant·Samuel Ogden·Amir Akbarnejad·Pietro Liò·Sattar Vakili·Mo Lotfollahi
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Abstract

High-throughput gene perturbation experiments can test several genetic interventions in parallel, yet experimental budgets remain limited. A central goal is hit discovery: identifying as many perturbations as possible whose phenotypic effect exceeds a predefined threshold. Pure exploration strategies are statistically inefficient, wasting budget on low-value regions. Bayesian optimization methods offer a principled alternative but target a single global optimum, over-exploiting dominant modes while neglecting other high-value regions. We formalize hit discovery as a sequential experimental design problem and propose Probability-of-Hit, an acquisition function that directly targets threshold exceedance by ranking candidates according to their posterior probability of being a hit. We prove asymptotic optimality of this approach and demonstrate strong empirical performance on both synthetic benchmarks and real biological immunology datasets, including up to 6.4% improvement over baselines on the Schmidt IL-2 dataset.

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Andrea Rubbi, Arpit Merchant, Samuel Ogden, Amir Akbarnejad, Pietro Liò, Sattar Vakili, Mo Lotfollahi. 2026-05-11. Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments. https://arxiv.org/abs/2605.10196

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