S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning.
Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest...
Main Authors: | Daniel R Schrider, Andrew D Kern |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2016-03-01
|
Series: | PLoS Genetics |
Online Access: | http://europepmc.org/articles/PMC4792382?pdf=render |
Similar Items
-
diploS/HIC: An Updated Approach to Classifying Selective Sweeps
by: Andrew D. Kern, et al.
Published: (2018-06-01) -
Adaptation in structured populations and fuzzy boundaries between hard and soft sweeps.
by: Yichen Zheng, et al.
Published: (2019-11-01) -
Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data.
by: Nandita R Garud, et al.
Published: (2021-02-01) -
Drug Resistance Evolution in HIV in the Late 1990s: Hard Sweeps, Soft Sweeps, Clonal Interference and the Accumulation of Drug Resistance Mutations
by: Kadie-Ann Williams, et al.
Published: (2020-04-01) -
On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn’t
by: Eran Elhaik, et al.
Published: (2021-04-01)