Sparse Bayesian classification and feature selection for biological expression data with high correlations.

ABSTRACT: Classification models built on biological expression data are increasingly used to predict distinct disease subtypes. Selected features that separate sample groups can be the candidates of biomarkers, helping us to discover biological functions/pathways. However, three challenges are associated with building a robust classification and feature selection model: 1) the number of significant biomarkers is much smaller than that of measured features for which the search will be exhaustive; 2) current biological expression data are big in both sample size and feature size which will worsen the scalability of any search algorithms; and 3) expression profiles of certain features are typically highly correlated which may prevent to distinguish the predominant features. Unfortunately, most of the e

SUBMITTER: Yang X 

PROVIDER: S-EPMC5744982 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature