Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

ABSTRACT: Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength

SUBMITTER: Chen G 

PROVIDER: S-EPMC4055569 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature