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Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

Author(s)
Hie, Brian; Bryson, Bryan D; Berger, Bonnie
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Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/
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Abstract
© 2020 The Author(s) A machine learning algorithm that also reports its certainty about a prediction can help a researcher design new experiments. Algorithms called Gaussian processes trained with modern data can make accurate predictions with informative uncertainty. We leverage this approach to find nanomolar kinase binders, Mycobacterium tuberculosis inhibitors, mutations that enhance protein fluorescence, and genes important for cell development.
Date issued
2020
URI
https://hdl.handle.net/1721.1/136149
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Biological Engineering; Ragon Institute of MGH, MIT and Harvard; Massachusetts Institute of Technology. Department of Mathematics
Journal
Cell Systems
Publisher
Elsevier BV

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