Next-Generation Machine Learning for Biological Networks
Name
Camacho_NextGenML_Cell_2018.pdf
Description
Accepted version
Size
891.17 KB
Format
Adobe PDF
Checksum (MD5)
d95927249f7e00dca2300ed0c8ded203
Author(s)
Collins, Katherine M.
Collins, James J.
Date Issued
June 2018
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
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.
Subjects
General Biochemistry, Genetics and Molecular Biology
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cell.2018.05.015
