Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

ABSTRACT:

Background

The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS) datasets, which involve millions of single nucleotide polymorphism (SNPs), where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more gener

SUBMITTER: Jiang X 

PROVIDER: S-EPMC4666609 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature