Empirical Bayes conditional independence graphs for regulatory network recovery.

ABSTRACT:

Motivation

Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods.

Methods

We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This re

SUBMITTER: Mahdi R 

PROVIDER: S-EPMC3400959 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature