Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts. (1st January 2011)

• Record Type: Journal Article
• Authors: Taylor, Ronald C.; Sanfilippo, Antonio; McDermott, Jason E.; Baddeley, Bob; Riensche, Roderick; Jensen, Russell; Verhagen, Marc; Pustejovsky, James
• Is Part Of: International journal of computational biology and drug design. Volume 4:Number 1(2011)
• Page Start: 56
• Page End: 82
• Publication Date: 2011-01-01
• Subjects: biological network inference -- network enrichment -- gene expression analysis -- bootstrap learning -- simulated annealing -- gene ontology -- computational biology -- PubMed -- regulatory networks; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• DOI: 10.1504/IJCBDD.2011.038657
• Languages: English
• ISSNs: 1756-0756

Abstract

Increasingly, reverse engineering methods have been employed to infer transcriptional regulatory networks from gene expression data. Enrichment with independent evidence from sources such as the biomedical literature and the Gene Ontology (GO) is desirable to corroborate, annotate and expand these networks as well as manually constructed networks. In this paper, we explore a novel approach for computer-assisted enrichment of regulatory networks. GO-based gene similarity is first tuned to an initial network augmented with gene links mined from PubMed and then used to drive network construction using a bootstrapping algorithm. We describe two applications of this approach and discuss its added value in terms of corroboration, annotation and expansion of manually constructed and reversed engineered networks.