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- W4321194906 endingPage "2574" @default.
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- W4321194906 abstract "Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts." @default.
- W4321194906 created "2023-02-18" @default.
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- W4321194906 date "2023-05-01" @default.
- W4321194906 modified "2023-10-18" @default.
- W4321194906 title "Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction" @default.
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- W4321194906 doi "https://doi.org/10.1109/jbhi.2023.3246225" @default.
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