Jian-Peng Zhou1, Lei Chen1,2, Zi-Han Guo1. 1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China. 2. Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, People's Republic of China.
Abstract
MOTIVATION: The anatomical therapeutic chemical (ATC) classification system plays an increasingly important role in drug repositioning and discovery. The correct identification of classes in each level of such system that a given drug may belong to is an essential problem. Several multi-label classifiers have been proposed in this regard. Although they provided satisfactory performance, the feature extraction procedures were still rough. More refined features may further improve the predicted quality. RESULTS: In this article, we provide a novel multi-label classifier, called iATC-NRAKEL, to predict drug ATC classes in the first level. To obtain more informative drug features, we employed the drug association information in STITCH and KEGG, which was organized by seven drug networks. The powerful network embedding algorithm, Mashup, was adopted to extract informative drug features. The obtained features were fed into the RAndom k-labELsets (RAKEL) algorithm with support vector machine as the basic classification algorithm to construct the classifier. The 10-fold cross-validation of the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 76.56 and 74.51%, respectively. The comparison results indicated that iATC-NRAKEL was much superior to all previous reported classifiers. Finally, the contribution of each network was analyzed. AVAILABILITY AND IMPLEMENTATION: The codes of iATC-NRAKEL are available at https://github.com/zhou256/iATC-NRAKEL.
MOTIVATION: The anatomical therapeutic chemical (ATC) classification system plays an increasingly important role in drug repositioning and discovery. The correct identification of classes in each level of such system that a given drug may belong to is an essential problem. Several multi-label classifiers have been proposed in this regard. Although they provided satisfactory performance, the feature extraction procedures were still rough. More refined features may further improve the predicted quality. RESULTS: In this article, we provide a novel multi-label classifier, called iATC-NRAKEL, to predict drug ATC classes in the first level. To obtain more informative drug features, we employed the drug association information in STITCH and KEGG, which was organized by seven drug networks. The powerful network embedding algorithm, Mashup, was adopted to extract informative drug features. The obtained features were fed into the RAndom k-labELsets (RAKEL) algorithm with support vector machine as the basic classification algorithm to construct the classifier. The 10-fold cross-validation of the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 76.56 and 74.51%, respectively. The comparison results indicated that iATC-NRAKEL was much superior to all previous reported classifiers. Finally, the contribution of each network was analyzed. AVAILABILITY AND IMPLEMENTATION: The codes of iATC-NRAKEL are available at https://github.com/zhou256/iATC-NRAKEL.
Authors: Shiqi Zhang; Tao Zeng; Bin Hu; Yu-Hang Zhang; Kaiyan Feng; Lei Chen; Zhibin Niu; Jianhao Li; Tao Huang; Yu-Dong Cai Journal: Front Bioeng Biotechnol Date: 2020-05-26
Authors: XiaoYong Pan; Tao Zeng; Fei Yuan; Yu-Hang Zhang; Lei Chen; LiuCun Zhu; SiBao Wan; Tao Huang; Yu-Dong Cai Journal: Front Bioeng Biotechnol Date: 2019-11-14
Authors: Min Li; XiaoYong Pan; Tao Zeng; Yu-Hang Zhang; Kaiyan Feng; Lei Chen; Tao Huang; Yu-Dong Cai Journal: Biomed Res Int Date: 2020-06-15 Impact factor: 3.411
Authors: Fei Yuan; XiaoYong Pan; Tao Zeng; Yu-Hang Zhang; Lei Chen; Zijun Gan; Tao Huang; Yu-Dong Cai Journal: Front Bioeng Biotechnol Date: 2020-04-29