Predicting Drug-Target Interactions Using Weisfeiler-Lehman Neural Network

被引：5
|
作者：
Manoochehri, Hafez Eslami [1 ]
Kadiyala, Susmitha Sri [1 ]
Nourani, Mehrdad [1 ]
机构：
[1] Univ Texas Dallas, Dept Elect & Comp Engn, Predict Analyt & Technol Lab, Richardson, TX 75083 USA
来源：
2019 IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL & HEALTH INFORMATICS (BHI) | 2019年
关键词：
Drug-Target Interaction; Link Prediction; Neural Network;
D O I：
10.1109/bhi.2019.8834572
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Predicting missing drug-target relationships can help to speed up the process of identifying unknown interactions between chemical drugs and target proteins in pharmaceutical research. In this paper we employ Weisfeiler-Lehman Neural Network method to capture features, purely based on topological network and learn the pattern of drug-target interactions. We show our approach is able to learn sophisticated drug-target topological features and outperform other similarity based methods in terms of AUROC.
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页数：4
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