Title: Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

Authors: Ronald C. Taylor, Antonio Sanfilippo, Jason E. McDermott, Bob Baddeley, Roderick Riensche, Russell Jensen, Marc Verhagen, James Pustejovsky

Addresses: Pacific Northwest National Laboratory, Richland, WA 99352, USA. ' Pacific Northwest National Laboratory, Richland, WA 99352, USA. ' Pacific Northwest National Laboratory, Richland, WA 99352, USA. ' Pacific Northwest National Laboratory, Richland, WA 99352, USA. ' Pacific Northwest National Laboratory, Richland, WA 99352, USA. ' Pacific Northwest National Laboratory, Richland, WA 99352, USA. ' Brandeis University, Waltham, MA 02454, USA. ' Brandeis University, Waltham, MA 02454, USA

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.

Keywords: biological network inference; network enrichment; gene expression analysis; bootstrap learning; simulated annealing; gene ontology; computational biology; PubMed; regulatory networks; gene similarity; gene links; biomedical literature; text mining.

DOI: 10.1504/IJCBDD.2011.038657

International Journal of Computational Biology and Drug Design, 2011 Vol.4 No.1, pp.56 - 82

Available online: 17 Feb 2011 *

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