Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.

ABSTRACT: Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such interm

SUBMITTER: Garcia-Alonso L 

PROVIDER: S-EPMC3488210 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature