Supervised Machine Learning for Population Genetics: A New Paradigm

By Daniel R. Schrider, Andrew D. Kern. Trends in Genetics, 2018

392 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/772c3f272882c75b6ec847b9502b625501ba3866

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Citations in the corpus

Spatial Population Genetics: It's About Time (2019, methodology): “ch theory may be lacking. In addition, these simulation methods can be used in statis24 tical inference. For example, simulated datasets can be used in the training of machine learning methods [e.g., Schrider and Kern, 2018], or to compare between dierent generative models using approximate Bayesian”
Efficient ancestry and mutation simulation with msprime 1.0 (2021, methodology): “…inference methods such as Approximate Bayesian Computation (ABC) (Beaumont et al. 2002; Csill (cid:2) ery et al. 2010; Wegmann et al. 2010) and machine learning-based approaches (Sheehan and Song 2016; Chan et al. 2018; Schrider and Kern 2018; Flagel et al. 2019; Sanchez et al. 2021).”