Simultaneous clustering of multiple gene expression and physical interaction datasets.

ABSTRACT: Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, usi

SUBMITTER: Narayanan M 

PROVIDER: S-EPMC2855327 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature