Next-Generation Machine Learning for Biological Networks

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 learn...

Full description

Bibliographic Details
Main Authors: Collins, Katherine M. (Author), Collins, James J. (Author)
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor)
Format: Article
Language:English
Published: Elsevier BV, 2020-04-23T14:33:46Z.
Subjects:
Online Access:Get fulltext
Description
Summary: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.