A unified frame of predicting side effects of drugs by using linear neighborhood similarity

BMC Systems Biology
Wen ZhangXining Zhang

Abstract

Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, an...Continue Reading

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Citations

Aug 22, 2018·Current Drug Metabolism·Wen ZhangYanqing Niu
Dec 19, 2019·BMC Medical Informatics and Decision Making·Ruoqi Liu, Ping Zhang
Dec 24, 2018·BMC Bioinformatics·Guifeng TangWen Zhang
May 7, 2020·Frontiers in Bioengineering and Biotechnology·Feng HuangFuchuan Ni
Jul 1, 2020·Briefings in Bioinformatics·A K M AzadFatemeh Vafaee
May 10, 2019·Frontiers in Genetics·Yi-Cheng GaoWen Zhang

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Software Mentioned

MSE
INBM
LNSM
SMI

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