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Supervised Machine Learning for Population Genetics: A New Paradigm
Open accessPublished version of a preprint
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.
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Method
Metadata
- Authors
- Schrider, D. R., & Kern, A. D.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Schrider, Daniel R.
- Kern, Andrew D.
- Venue
- Trends in Genetics
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 34
- Issue
- 4
- Pages
- 301-312
- ISSN
- 0168-9525 (ISSN-L)
- PMC
- PMC5905713
- OpenAlex
- W2783880833
- Cited by
- 453
- References
- 110
- Citations by year
- 2026: 41 · 2025: 55 · 2024: 51 · 2023: 50 · 2022: 68 · 2021: 65 · 2020: 57 · 2019: 46 · 2018: 20
- FWCI
- 6.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Trends in Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (K99HG008696)
- National Institute of General Medical Sciences (R01GM117241)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- supervised learning, population genetics, population genetic inference, genome sequencing data, evolutionary genomics
- MeSH terms
- Supervised Machine Learning, Biological Evolution, Genetics, Population, Humans, Selection, Genetic, Genome, Human, Data Mining, Datasets as Topic
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