Work

Supervised Machine Learning for Population Genetics: A New Paradigm

Year: 2018

Type: review

Abstract: As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.

Source: Trends in Genetics

DOI: 10.1016/j.tig.2017.12.005

Authors: Daniel R. Schrider, Andrew D. Kern

Institution: Rutgers, The State University of New Jersey

Language: English

Topic: Genetic diversity and population structure

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $7,030

Funders: National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences