Title: Drug-drug interaction prediction based on co-medication patterns and graph matching

Authors: Wen-Hao Chiang; Li Shen; Lang Li; Xia Ning

Addresses: Department of Computer and Information Science, Indiana University – Purdue University Indianapolis, Indianapolis, IN, 46202, USA ' Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA ' Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA ' Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA

Abstract: High-order drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) are common, particularly for elderly people, and therefore represent a significant public health problem. In this paper, the problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered. To solve this problem, novel kernels over drug combinations of arbitrary orders are developed within support vector machines (SVMs) for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest.

Keywords: drug-drug interaction prediction; drug combination similarity; comedication; graph matching; arbitrary order; adverse drug reaction; myopathy; single drug similarity; SVMs; support vector machines; binary classification problem.

DOI: 10.1504/IJCBDD.2020.105093

International Journal of Computational Biology and Drug Design, 2020 Vol.13 No.1, pp.36 - 57

Received: 07 Jun 2018
Accepted: 15 Aug 2018
Published online: 13 Feb 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article