Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

ABSTRACT: The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the t

SUBMITTER: Himmelstein DS 

PROVIDER: S-EPMC4497619 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature