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- W3176821806 abstract "Diseases are usually caused by body’s own defects protein or the functional structure of viral proteins. Effective drugs can be combined with these proteins well and remove original functions to achieve the therapeutic effect. The biochemical approaches of drug-target interactions (DTIs) determination is expensive and time-consuming. Therefnal-based methods have been proposed to predict new DTIs. In order to solve the problem of multiple information fusion, we propose a multi-view graph regularized link propagation model (MvGRLP) to predict new DTIs. Multi-view learning could use the complementary and correlated information between different views (features). Compared with existing models, our method achieves comparable and best results on four benchmark datasets." @default.
- W3176821806 created "2021-07-05" @default.
- W3176821806 creator A5001619694 @default.
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- W3176821806 date "2021-10-01" @default.
- W3176821806 modified "2023-10-14" @default.
- W3176821806 title "Identification of drug-target interactions via multi-view graph regularized link propagation model" @default.
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- W3176821806 doi "https://doi.org/10.1016/j.neucom.2021.05.100" @default.
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