期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 88, 期 -, 页码 90-97出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2018.11.005
关键词
Manifold regularization; Drug-drug interaction prediction; Matrix completion
资金
- National Natural Science Foundation of China [61772381, 61572368]
- Fundamental Research Funds for the Central Universities [204201710219]
- National Key Research and Development Program [2018YFC0407904]
Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery. Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs, and is important for the drug safety surveillance. Here, we formulate the drug-drug interaction prediction as a matrix completion task, and project drugs in the interaction space into a low-dimensional space. We consider drug features, i.e., substructures, targets, enzymes, transporters, pathways, indications, side effects, and off side effects, to calculate drug-drug similarities, and assume them as manifolds in feature spaces. In this paper, we present a novel computational method named Manifold Regularized Matrix Factorization (MRMF) to predict potential drug-drug interactions, by introducing the drug feature-based manifold regularization into the matrix factorization. In the computational experiments, the MRMF models, which utilize known drug-drug interactions and the drug feature-based manifold, produce the area under precision-recall curves (AUPR) up to 0.7963. We test manifold regularizations based on different drug features, and the MRMF models can produce robust performances. Compared with other state-of-the-art methods, the MRMF models can produce better performances in the cross validation and case study. The manifold regularization is the critical factor for the high-accuracy performances of our method. MRMF is promising and effective for the prediction of drug-drug interactions.
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