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Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens

arXiv q-bio.QM Bioinformatics AI Carl Edwards, Edward De Brouwer, Xiner Li, Namkyeong Lee, Ehsan Hajiramezanali, Anne Biton, Sara Mostafavi, Gabriele Scalia 2026-09-10
Representative image for Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens

TL;DR - AssayLoop combines a transformer-based acquisition policy with LLM-derived biological priors to prioritize CRISPR perturbations across sequential experiments. It discovers 27.7% of hits while testing about 5% of candidates, potentially making constrained biological screens substantially more efficient.

  • AssayBench-Loop includes 1,389 CRISPR screens spanning five phenotype categories.
  • AssayFormer learns adaptive acquisition strategies from historical screens and updates them using experimental feedback.
  • An adaptive handoff integrates LLM priors, while AssayLLM extends the approach through task-specific LLM post-training.
  • On temporally held-out screens, AssayLoop achieved 5.67Ă— enrichment over random selection and transferred to phenotype categories excluded from training.

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