ARWAR: a Network Approach for Predicting Adverse Drug Reactions.
Research output: Contribution to journal › Article › Academic › peer-review
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. (C) 2015 Elsevier Ltd. All rights reserved.
- Side effects, Adverse Drug Reactions, Network approach, Human Drug Network, Multi-label classification, Graph augmentation, Random Walk with Restarts, DISEASE ASSOCIATIONS, IDENTIFICATION, INTEGRATION, PROFILES, MODEL