计算机科学
人工智能
机器学习
深度学习
数据挖掘
数据科学
作者
Kaiqi Yuan,Yilin Zhang,Yaliang Li,Hai-Tao Zheng,Ying Shen
出处
期刊:IEEE Transactions on Big Data
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号: (01): 1-1
标识
DOI:10.1109/tbdata.2021.3051673
摘要
Motivation: The prediction of drug-target interaction (DTI) from heterogeneous biological data is critical to predict drugs and therapeutic targets for known diseases such as tumor and bowel disease. The study of DTI based on drug representation learning can strengthen or integrate our knowledge of pharmacological and chemical phenomena. Therefore, there is a strong motivation to develop effective methods that can detect these potential drug-target interactions. Results: We have developed a novel Knowledge-Enhanced Multi-View framework (KEMV) to predict unknown DTIs from pharmacological data and chemical data on a large scale. The proposed method consists of two steps: (i) learning more comprehensive drug representations via the proposed multi-view attention mechanism, which bridges pharmacological and chemical information, and interactively summarizes the attention values depending on varying interactions between different pairs of drug features. (ii) predicting unknown drug-target interactions based on the drug and target representations. The method is tested on real-world dataset KEGG with three classes of important drug–target interactions involving enzymes, ion channels, and G-protein-coupled receptors. Our framework is proven to uncover potential DTIs with scientific evidences explaining the mechanism of the interactions through the processing of high-dimensional, heterogeneous, and sparse drug data. Availability: Our code is available at: https://github.com/YuanKQ/DTI-Prediction
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