Forecasting labels under distribution-shift for machine-guided sequence designDownload PDF

09 Oct 2022 (modified: 05 May 2023)LMRL 2022 PaperReaders: Everyone
Keywords: forecasting, distribution shift, biological sequence design
TL;DR: A forecasting algorithm using calibration and simulation produces high-quality forecasts of top labels in libraries of ML-designed biological sequences.
Abstract: 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.
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