Identification of drug-side effect association via multiple information integration with centered kernel alignment

In medicine research, drug discovery aims to develop a drug to patients who will benefit from it and try to avoid some side effects. However, the tradition experiment is time consuming and expensive. In recent years, computational approaches provide many effective strategies to deal with this issue. In fact, the known associations between drugs and side-effects are less than unknown associations, thus it can be seen as an imbalance classification problem. Although several classification methods have been developed to predict drug-side effect associations, the performance of predictors could also be further improved. In this paper, we propose a novel predictor of drug-side effect associations. First, we construct multiple kernels from drug space and side-effect space, respectively. Then, these corresponding kernels are linear weighted by optimized Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL) algorithm in two different spaces. At last, Kronecker Regularized Least Squares (Kronecker RLS) is employed to fuse drug kernel and side-effect kernel, further identify drug-side effect associations. Compared with many existing methods, our proposed approach achieves better results on three benchmark datasets of drug side-effect associations. The values of Area Under the Precision Recall curve (AUPR) are 0.672, 0.679 and 0.675 on Pauwels’s dataset, Mizutani’s dataset and Liu’s dataset, respectively. The AUPRs are improved by at least 0.012, 0.013 and 0.014 on three different datasets. Experimental results show that our method has outstanding performance among other excellent approaches on identifying drug-side effect associations.