Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

• URL: http://arxiv.org/abs/2210.10784v1
• Date: Wed, 19 Oct 2022 12:33:06 GMT
• Title: Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction
• Authors: Stuti Jain, Emilie Chouzenoux, Kriti Kumar, and Angshul Majumdar
• Abstract summary: 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. This paper presents a novel Graph Regularized Proabilistic Matrix Factorization (MF) method, which incorporates expert knowledge through a novel graph-based regularization strategy.
• Score: 18.659559002642784
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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