title: Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts.
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.
authors.affiliation: Pacific Northwest National Laboratory, Richland, WA 99352, USA. Ronald.Taylor@pnl.gov
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Taylor
authors.forename: Antonio
authors.initials: A
authors.lastname: Sanfilippo
authors.forename: Jason E
authors.initials: JE
authors.lastname: McDermott
authors.forename: Bob
authors.initials: B
authors.lastname: Baddeley
authors.forename: Roderick
authors.initials: R
authors.lastname: Riensche
authors.forename: Russell
authors.initials: R
authors.lastname: Jensen
authors.forename: Marc
authors.initials: M
authors.lastname: Verhagen
authors.forename: James
authors.initials: J
authors.lastname: Pustejovsky
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D004926:Escherichia coli; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D005798:Genes, Bacterial / Q000235:genetics / Q000502:physiology; D008957:Models, Genetic; D039781:PubMed