Balanced Functional Module Detection in genomic data.

ABSTRACT:

Motivation

High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an effi

SUBMITTER: Tritchler D 

PROVIDER: S-EPMC9710612 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature