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Next-Generation Machine Learning for Biological Networks

Author(s)
Collins, Katherine M.; Collins, James J.
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Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/
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Abstract
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enablingone to generate models that learn from large datasets and make predictions on likely outcomes,machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research,and synthetic biology.
Date issued
2018-06
URI
https://hdl.handle.net/1721.1/124832
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Department of Biological Engineering
Journal
Cell
Publisher
Elsevier BV
Citation
Camacho, Diogo M. et al. “Next-Generation Machine Learning for Biological Networks.” Cell 173 (2018) © 2018 The Author(s)
Version: Author's final manuscript
ISSN
0092-8674
Keywords
General Biochemistry, Genetics and Molecular Biology

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