Identification of Drug-Side-Effect Association via Multiview Semisupervised Sparse Model

被引：7

作者：

Ding Y. ^{[1
]}

Guo F. ^{[2
]}

Tiwari P. ^{[3
]}

Zou Q. ^{[4
]}

机构：

[1] University of Electronic Science and Technology of China, Yangtze Delta Region Institute (Quzhou), Quzhou

[2] Central South University, School of Computer Science and Engineering, Changsha

[3] Halmstad University, School of Information Technology, Halmstad

[4] University of Electronic Science and Technology of China, Institute of Fundamental and Frontier Sciences, Chengdu

来源：

IEEE Transactions on Artificial Intelligence | 2024年 / 5卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Bipartite network; drug side effects; multiview learning (MVL); similarity matrix; sparse model;

D O I：

10.1109/TAI.2023.3314405

中图分类号：

学科分类号：

摘要：

The association between drugs and side effects encompasses information about approved medications and their documented adverse drug reactions. Traditional experimental approaches for studying this association tend to be time consuming and expensive. To represent all drug-side-effect associations, a bipartite network framework is employed. Consequently, numerous computational methods have been devised to tackle this problem, focusing on predicting new potential associations. However, a significant gap lies in the neglect of the multiview learning algorithm, which has the ability to integrate diverse information sources and enhance prediction accuracy. In our study, we have developed a novel predictor named multiview semisupervised sparse model (Mv3SM) to address the drug side effect prediction problem. Our approach aims to explore the distinctive characteristics of various view features obtained from fully paired multiview data and mitigate the influence of noisy data. To test the performance of Mv3SM and other computational approaches, we conducted experiments using three benchmark datasets. The obtained results clearly demonstrate that our proposed method achieves superior predictive performance compared to alternative approaches. © 2023 IEEE.

引用

页码：2151 / 2162

页数：11

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