Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Dec 15, 2015
Abstract
MOTIVATION: Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness).