Abstract
Drug-drug interaction (DDI) prediction is a challenging problem in drug development and disease treatment. Current computational studies mainly solve this problem by designing features and extracting features from multi-sources. They have limitations in accuracy, knowledge and universality. In this paper, a universal computational method for DDI prediction is proposed. The model is made up of several identical layers. Each layer is composed by a multi-attention unit and a capsule unit. Multi-attention is responsibility for extracting neighborhood features and capturing representation under various semantic relation. Capsule unit is responsibility for features aggregating. Each layer can be viewed as first-order representation. High-order representation is obtained by increasing layers. The structure of whole method is easily understood and implemented. Experiment results show that proposed method can achieve a promising predicting performance and stands out when compared with several popular network embedding methods. Proposed method is a reliable model for DDI prediction and is suitable for other interaction predictions.
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Funding
This work was supported in part by Awardee of the NSFC Excellent Young Scholars Program, under Grant 61722212, in part by the National Natural Science Foundation of China, under Grants 61702444, in part by the Chinese Postdoctoral Science Foundation, under Grant 2019M653804, in part by the West Light Foundation of The Chinese Academy of Sciences, under Grant 2018-XBQNXZ-B-008.
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Su, XR., You, ZH., Yi, HC., Zhao, BW. (2021). Detection of Drug-Drug Interactions Through Knowledge Graph Integrating Multi-attention with Capsule Network. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_38
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