High dimensional model representation of log likelihood ratio: binary classification with SNP data.

ABSTRACT:

Background

Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms.

Methods

We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We comp

SUBMITTER: Pour AF 

PROVIDER: S-EPMC7504683 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature