title: ARWAR: A network approach for predicting Adverse Drug Reactions.
abstract: Predicting novel drug side-effects, or Adverse Drug Reactions (ADRs), plays an important role in the drug discovery process. Existing methods consider mainly the chemical and biological characteristics of each drug individually, thereby neglecting information hidden in the relationships among drugs. Complementary to the existing individual methods, in this paper, we propose a novel network approach for ADR prediction that is called Augmented Random-WAlk with Restarts (ARWAR). ARWAR, first, applies an existing method to build a network of highly related drugs. Then, it augments the original drug network by adding new nodes and new edges to the network and finally, it applies Random Walks with Restarts to predict novel ADRs. Empirical results show that the ARWAR method presented here outperforms the existing network approach by 20% with respect to average Fmeasure. Furthermore, ARWAR is capable of generating novel hypotheses about drugs with respect to novel and biologically meaningful ADR.
authors.affiliation: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran; Maastricht University, PO Box 616, Maastricht 6200 MD, The Netherlands. Electronic address: h.rahmani@maastrichtuniversity.nl.
authors.forename: Hossein
authors.initials: H
authors.lastname: Rahmani
authors.affiliation: Maastricht University, PO Box 616, Maastricht 6200 MD, The Netherlands.
authors.forename: Gerhard
authors.initials: G
authors.lastname: Weiss
authors.affiliation: Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.
authors.forename: Oscar
authors.initials: O
authors.lastname: Méndez-Lucio
authors.affiliation: Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
mesh_terms: D000818:Animals; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D007255:Information Services
keywords: Adverse Drug Reactions; Graph augmentation; Human Drug Network; Multi-label classification; Network approach; Random Walk with Restarts; Side effects