Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV)

Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV)

Abstract Background Machine learning methods and conventions are increasingly employed for the analysis of large, complex biomedical data sets, including genome-wide association studies (GWAS). Reproducibility of machine learning analyses of GWAS can be hampered by biological and statistical factors...

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Bibliographic Details
Main Authors: Elizabeth R. Piette, Jason H. Moore
Format: Article
Language:English
Published: BMC 2018-04-01
Series:BioData Mining
Subjects:
Cross validation
Machine learning
Epistasis
GWAS
Online Access:http://link.springer.com/article/10.1186/s13040-018-0167-7

Internet

http://link.springer.com/article/10.1186/s13040-018-0167-7

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