Prediction of drug side effects with transductive matrix co-completion

Author:

Liang Xujun12ORCID,Fu Ying1,Qu Lingzhi1,Zhang Pengfei1,Chen Yongheng12

Affiliation:

1. NHC Key Laboratory of Cancer Proteomics, Department of Oncology

2. National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University , Changsha 410008, China

Abstract

Abstract Motivation Side effects of drugs could cause severe health problems and the failure of drug development. Drug–target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. Results We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug–target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. Availability and implementation The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Natural Science Foundation of Hunan Province

National Natural Science Foundation of Hunan Province

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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