文献类型：会议论文

英文题名：Drug-Target Interaction Prediction with Weighted Bayesian Ranking

作者：Shi, Zezhi[1];Li, Jianhua[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

会议论文集：2nd International Conference on Biomedical Engineering and Bioinformatics (ICBEB)

会议日期：SEP 19-21, 2018

会议地点：Tianjin, PEOPLES R CHINA

语种：英文

外文关键词：Drug-target interactions prediction; weighted Bayesian ranking; dual similarity regularization; novel drugs and targets

摘要：Identifying drug-target interactions (DTIs) through biochemical experiments is very expensive and time-consuming. Therefore, it is an inevitable trend to use computational methods to predict the drug-target interactions, and high prediction accuracy becomes our ultimate goal. However, most existing computational methods treat the non-interaction data as negative samples which is unreasonable as those non-interaction data may contain undetected drug-target interactions. In this paper, a novel weighted Bayesian ranking method (WBRDTI) for drug-target interactions prediction is proposed, and the different effects of each drug-target pair also is taken into account. Besides, dual similarity is used to regularize the latent factors of drugs and targets respectively, and known neighbor information is used to smooth novel drug or target. Finally, the experiment results on widely used publicly available drug-target interaction datasets show its effectiveness and the practicality of the proposed method.