Forecasting labels under distribution-shift for machine-guided sequence design

• DOI: 10.48550/arxiv.2211.10422
• Publication Date: 2022-01-01
• Authors: Wheelock, Lauren ...; Malina, Stephen; Gerold, Jeffrey; Sinai, Sam
• Keywords: 0301 basic medicine; FOS: Computer and information sciences; Computer Science - Machine Learning; Biomolecules (q-bio.BM); Quantitative Biology - Quantitative Methods; Machine Learning (cs.LG); 03 medical and health sciences; Quantitative Biology - Biomolecules; Optimization and Control (math.OC); FOS: Biological sciences; FOS: Mathematics; Mathematics - Optimization and Control; Quantitative Methods (q-bio.QM)

15 pages, 3 figures, to appear in MLCB-PMLR proceedings, oral presentation at MLCB 2022 and LMLR 2022<br/>The ability to design and optimize biological sequences with specific functionalities would unlock enormous value in technology and healthcare. In recent years, machine learning-guided sequence design has progressed this goal significantly, though validating designed sequences in the lab or clinic takes many months and substantial labor. It is therefore valuable to assess the likelihood that a designed set contains sequences of the desired quality (which often lies outside the label distribution in our training data) before committing resources to an experiment. Forecasting, a prominent concept in many domains where feedback can be delayed (e.g. elections), has not been used or studied in the context of sequence design. Here we propose a method to guide decision-making that forecasts the performance of high-throughput libraries (e.g. containing $10^5$ unique variants) based on estimates provided by models, providing a posterior for the distribution of labels in the library. We show that our method outperforms baselines that naively use model scores to estimate library performance, which are the only tool available today for this purpose.<br/>