Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery.

ABSTRACT: In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein-protein interaction network (PPI, or interactome) to predict novel disease-disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome netwo

SUBMITTER: Paci P 

PROVIDER: S-EPMC7819998 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature