Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

ABSTRACT:

Background

A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.

Results

We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, call

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC4137727 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature