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Machine Learning for Biological Design.

Authors :
Blau T
Chades I
Ong CS
Source :
Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2024; Vol. 2760, pp. 319-344.
Publication Year :
2024

Abstract

We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives when designing better experiments: to improve the predictive model or to improve the experimental outcome. We survey five different approaches for adaptive experimental design that iteratively search the space of possible experiments while adapting to measured data. The approaches are Bayesian optimization, bandits, reinforcement learning, optimal experimental design, and active learning. These machine learning approaches have shown promise in various areas of biology, and we provide broad guidelines to the practitioner and links to further resources.<br /> (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Details

Language :
English
ISSN :
1940-6029
Volume :
2760
Database :
MEDLINE
Journal :
Methods in molecular biology (Clifton, N.J.)
Publication Type :
Academic Journal
Accession number :
38468097
Full Text :
https://doi.org/10.1007/978-1-0716-3658-9_19