Integrated edge information and pathway topology for drug-disease associations
Xianbin Li,
Xiangzhen Zan,
Tao Liu,
Xiwei Dong,
Haqi Zhang,
Qizhang Li,
Zhenshen Bao,
Jie Lin
Affiliations
Xianbin Li
School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China; Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Corresponding author
Xiangzhen Zan
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong 520000, China
Tao Liu
School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
Xiwei Dong
School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
Haqi Zhang
Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
Qizhang Li
Innovative Drug R&D Center, School of Life Sciences, Huaibei Normal University, Huaibei, Anhui 235000, China
Zhenshen Bao
College of Information Engineering, Taizhou University, Taizhou 225300, Jiangsu, China
Jie Lin
Department of Pharmacy, the Third Affiliated Hospital of Wenzhou Medical University, Wenzhou 325200, Zhejiang Province, China; Corresponding author
Summary: Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.
