A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning
AuthorSun, Y; Hameed, PN; Verspoor, K; Halgamuge, S
Source TitleBMC SYSTEMS BIOLOGY
PublisherBIOMED CENTRAL LTD
University of Melbourne Author/sHalgamuge, Saman; Verspoor, Cornelia; Sun, Yahui; Hameed, Pathima Nusrath
Document TypeConference Proceeding
CitationsSun, Y; Hameed, PN; Verspoor, K; Halgamuge, S, A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning, BMC SYSTEMS BIOLOGY, 2016, 10
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5249043
BACKGROUND: Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning. RESULTS: Drug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases. CONCLUSIONS: We find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.
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