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bioRxiv · 10.1101/2022.10.18.512676

Graph Regularized Probabilistic MatrixFactorization for Drug-Drug Interactions Prediction

Jain, S.·Chouzenoux, E.·Kumar, K.·Majumdar, A.
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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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Jain, S., Chouzenoux, E., Kumar, K., Majumdar, A.. 2022-10-21. Graph Regularized Probabilistic MatrixFactorization for Drug-Drug Interactions Prediction. https://doi.org/10.1101/2022.10.18.512676

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