iATC-NRAKEL: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs | Zendy

Jianpeng Zhou | Zendy; Lei Chen | Zendy; Zihan Guo | Zendy

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iATC-NRAKEL: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs

Author(s) -

Jianpeng Zhou,

Lei Chen,

Zihan Guo

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz757

Subject(s) - computer science , classifier (uml) , artificial intelligence , support vector machine , random forest , machine learning , pattern recognition (psychology) , data mining , feature extraction

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.

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