Inferring cluster-based networks from differently stimulated multiple time-course gene expression data.

ABSTRACT:

Motivation

Clustering and gene network inference often help to predict the biological functions of gene subsets. Recently, researchers have accumulated a large amount of time-course transcriptome data collected under different treatment conditions to understand the physiological states of cells in response to extracellular stimuli and to identify drug-responsive genes. Although a variety of statistical methods for clustering and inferring gene networks from expression profiles have been proposed, most of these are not tailored to simultaneously treat expression data collected under multiple stimulation conditions.

Results

We propose a new statistical method for analyzing temporal profiles under multiple experimental conditions. Our method simultaneously performs clustering o

SUBMITTER: Shiraishi Y 

PROVIDER: S-EPMC2853688 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature