Oncotarget

Research Papers:

iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals

Xiang Cheng, Shu-Guang Zhao, Xuan Xiao _ and Kuo-Chen Chou

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Oncotarget. 2017; 8:58494-58503. https://doi.org/10.18632/oncotarget.17028

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Abstract

Xiang Cheng1,2, Shu-Guang Zhao1, Xuan Xiao2,3 and Kuo-Chen Chou3,4,5

1College of Information Science and Technology, Donghua University, Shanghai 201620, China

2Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333001, China

3Gordon Life Science Institute, Boston, MA 02478, USA

4Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China

5Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia

Correspondence to:

Xuan Xiao, email: xxiao@gordonlifescience.org

Kuo-Chen Chou, email: kcchou@gordonlifescience.org

Keywords: ATC classification, drug ontology, multi-label system, Chou’s five intuitive metrics

Received: February 08, 2017    Accepted: March 28, 2017    Published: April 11, 2017

ABSTRACT

Recommended by the World Health Organization (WHO), drug compounds have been classified into 14 main ATC (Anatomical Therapeutic Chemical) classes according to their therapeutic and chemical characteristics. Given an uncharacterized compound, can we develop a computational method to fast identify which ATC class or classes it belongs to? The information thus obtained will timely help adjusting our focus and selection, significantly speeding up the drug development process. But this problem is by no means an easy one since some drug compounds may belong to two or more than two ATC classes. To address this problem, using the DO (Drug Ontology) approach based on the ChEBI (Chemical Entities of Biological Interest) database, we developed a predictor called iATC-mDO. Subsequently, hybridizing it with an existing drug ATC classifier, we constructed a predictor called iATC-mHyb. It has been demonstrated by the rigorous cross-validation and from five different measuring angles that iATC-mHyb is remarkably superior to the best existing predictor in identifying the ATC classes for drug compounds. To convenience most experimental scientists, a user-friendly web-server for iATC-mHyd has been established at http://www.jci-bioinfo.cn/iATC-mHyb, by which users can easily get their desired results without the need to go through the complicated mathematical equations involved.


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