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• W4226227861 abstract "Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data about drugs and targets, however, is a challenging issue. We propose a drug-target interaction (DTI) prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections. A drug-protein-disease heterogeneous network (RPD-Net) is firstly constructed to associate diverse similarities, interactions and associations across nodes. Secondly, we propose a multi-scale pairwise deep representation learning module consisting of a new embedding strategy to integrate diverse inter-relations and intra-relations, and dilation convolutions for multi-scale deep representation extraction. A global topology learning module is proposed which is composed of strategy based on non-negative matrix factorization (NMF) to extract topology from RPD-Net, and a new relational-level attention mechanism for discriminative topology embedding. Experimental results using public dataset demonstrate improved performance over state-of-the-art methods and contributions of our major innovations. Evaluation results by top k recall rates and case studies on five drugs further show the effectiveness of our method in retrieving potential target candidates for drugs." @default.
• W4226227861 created "2022-05-05" @default.
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• W4226227861 date "2022-04-01" @default.
• W4226227861 modified "2023-10-18" @default.
• W4226227861 title "Learning multi-scale heterogeneous representations and global topology for drug-target interaction prediction" @default.
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• W4226227861 doi "https://doi.org/10.1109/jbhi.2021.3121798" @default.
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