Attribute Supervised Probabilistic Dependent Matrix Tri-Factorization Model for the Prediction of Adverse Drug-Drug Interaction

Zhu, Jiajing; Liu, Yongguo; Zhang, Yun; Li, Dongxiao

doi:10.1109/jbhi.2020.3048059

Cited by 24 publications

(7 citation statements)

References 38 publications

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“…They defined A = U Σ U T = U normalΣ Σ T U T = A d A T d , where A d denotes a low-dimensional latent interaction matrix, of which each row represents the feature vector of a drug in the latent space, and the inner products between drugs are positively correlated with their interactivity. Zhu et al presented an attribute-supervised learning model, named Probabilistic Dependent Matrix Tri-Factorization (PDMTF), to predict adverse DDIs. In particular, PDMTF models adverse drug interactions by combining two pharmacological properties, molecular structure, side effects, and their associations.…”

Section: Research Approaches For Ddi Predictionmentioning

confidence: 99%

Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Wang,

Zhu,

et al. 2023

J. Chem. Inf. Model.

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Detecting drug−drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

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Section: Research Approaches For Ddi Predictionmentioning

confidence: 99%

Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Wang,

Zhu,

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Here, the comprehensive DDI matrix is a signed binary matrix with +1 for enhancive drugs, −1 for degressive drugs, and 0 for no drug interactions, respectively, which is rather useful to predict the (positive/negative) behaviors of the interacting drugs. The work in [25] presents an attribute supervised learning model probabilistic dependent matrix tri-factorization (PDMTF) approach for adverse DDI prediction. They utilized two drug attributes, molecular structure, side effects, and their correlation to compute the adverse interactions among drugs.…”

Section: Related Workmentioning

confidence: 99%

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Jain

Chouzenoux

Kumar

et al. 2022

Preprint

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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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“…There are three common methods for constructing networks. Factorization methods [ 17 , 18 ] decompose the known DDI matrix into several low-dimensional matrices and reconstruct them for prediction tasks. For example, Yu et al [ 19 ] developed a DDINMF method based on semi-nonnegative matrix factorization.…”

Section: Introductionmentioning

confidence: 99%

Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships

et al. 2022

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Drug–drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings for prediction will be obtained. To address this limitation, we propose a multitype drug interaction prediction method based on the deep fusion of drug features and topological relationships, abbreviated DM-DDI. The proposed method adopts a deep fusion strategy to combine drug features and topologies to learn representative drug embeddings for DDI prediction. Specifically, a deep neural network model is first used on the drug feature matrix to extract feature information, while a graph convolutional network model is employed to capture structural information from the adjacency matrix. Then, we adopt delivery operations that allow the two models to exchange information between layers, as well as an attention mechanism for a weighted fusion of the two learned embeddings before the output layer. Finally, the unified drug embeddings for the downstream task are obtained. We conducted extensive experiments on real-world datasets, the experimental results demonstrated that DM-DDI achieved more accurate prediction results than state-of-the-art baselines. Furthermore, in two tasks that are more similar to real-world scenarios, DM-DDI outperformed other prediction methods for unknown drugs.

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Attribute Supervised Probabilistic Dependent Matrix Tri-Factorization Model for the Prediction of Adverse Drug-Drug Interaction

Cited by 24 publications

References 38 publications

Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships

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