JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

ABSTRACT:

Motivation

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.

Results

We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation betw

SUBMITTER: Ji J 

PROVIDER: S-EPMC5870609 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature