Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 23, Issue 6, Pages 2619-2632Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2883834
Keywords
Drugs; Kernel; Prediction algorithms; Chemicals; Informatics; Semisupervised learning; Symmetric matrices; Drug-side effect; bipartite network; multiple kernel learning; graph-based learning; semi-supervised learning
Categories
Funding
- National Science Foundation of China [NSFC 61772362]
- Tianjin Research Program of Application Foundation and Advanced Technology [16JCQNJC00200]
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Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite network. Therefore, many computational approaches have been developed to deal with this problem, which are used to predict new potential associations. However, lots of methods did not consider multiple kernel learning (MKL) algorithm, which can integrate multiple sources of information and further improve prediction performance. In this study, we develop a novel predictor of drug-side effect association. First, we build multiple kernels from drug space and side-effect space. What is more, these corresponding kernels are linear weighted by MKL algorithm in drug space and side-effect space, respectively. Finally, a graph-based semisupervised learning is employed to construct drug-side effect predictor. Compared with existing methods, our method achieves better results on three benchmark data sets. The values of area under the precision recall curve are 0.668, 0.673, and 0.670 on three benchmark data sets, respectively. Our method is a useful tool for the side-effects prediction of drugs.
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