Author ORCID Identifier
Yitong Huang: 0000-0002-5200-8077
Document Type
Article
Publication Date
8-1-2020
Publication Title
Current Opinion in Systems Biology
Abstract
In the past few decades, mathematical models based on dynamical systems theory have provided new insight into diverse biological systems. In this review, we ask whether the recent success of machine learning techniques for large-scale biological data analysis provides a complementary or competing approach to more traditional modeling approaches. Recent applications of machine learning to the problem of learning biological dynamics in diverse systems range from neuroscience to animal behavior. We compare the underlying mechanisms and limitations of traditional dynamical models with those of machine learning models. We highlight the unique role that traditional modeling has played in providing predictive insights into biological systems, and we propose several avenues for bridging traditional dynamical systems theory with large-scale analysis enabled by machine learning.
Keywords
Machine learning, Neural networks, Systems biology, Unsupervised learning
Volume
22
First Page
1
Last Page
7
DOI
10.1016/j.coisb.2020.07.009
Recommended Citation
Gilpin, William; Huang, Yitong; and Forger, Daniel B., "Learning Dynamics from Large Biological Data Sets: Machine Learning Meets Systems Biology" (2020). Mathematics Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/mth_facpubs/194
Comments
Archived as published.