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BioMed Research International
Volume 2016, Article ID 2375268, 12 pages
http://dx.doi.org/10.1155/2016/2375268
Research Article

In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

1Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian 350122, China
2College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471023, China
3School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
4College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
5School of Computer Science and Technology, Heilongjiang University, Harbin, Heilongjiang 150080, China
6School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China

Received 24 April 2016; Revised 8 June 2016; Accepted 19 June 2016

Academic Editor: Yungang Xu

Copyright © 2016 Zhijun Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Zhijun Liao, Yong Huang, Xiaodong Yue, Huijuan Lu, Ping Xuan, and Ying Ju, “In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches,” BioMed Research International, vol. 2016, Article ID 2375268, 12 pages, 2016. https://doi.org/10.1155/2016/2375268.