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, particularly so for the investigation of non-additive genetic interactions. Application of traditional cross validation to a GWAS data set may result in poor consistency between the training and testing data set splits due to an imbalance of the interaction genotypes relative to the data as a whole. We propose a new cross validation method, proportional instance cross validation (PICV), that preserves the original distribution of an independent variable when splitting the data set into training

SUBMITTER: Piette ER 

PROVIDER: S-EPMC5907739 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature