Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles.

ABSTRACT:

Background

Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes.

Results

We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifie

SUBMITTER: Aibar S 

PROVIDER: S-EPMC4460584 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature