Conformal prediction under feedback covariate shift for biomolecular design.

ABSTRACT: Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next. For example, a data-driven approach for designing proteins is to train a regression model to predict the fitness of protein sequences and then use it to propose new sequences believed to exhibit greater fitness than observed in the training data. Since validating designed sequences in the wet laboratory is typically costly, it is important to quantify the uncertainty in the model's predictions. This is challenging because of a characteristic type of distribution shift between the training and test data that arises in the design setting-one in which the training and test data are statistical

SUBMITTER: Fannjiang C 

PROVIDER: S-EPMC9618043 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature