期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 42, 期 4, 页码 504-507出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2012.01.003
关键词
Voltage-gated potassium channel; Subfamily; Feature selection; Support vector machine
类别
资金
- National Natural Science Foundation of China [61100092]
- Scientific and Technological Department Foundation of Hebei Province [11275532]
- Doctoral Scientific Research Start-up Foundation of Hebei United University [10101115]
- Fundamental Research Funds for the Central Universities [ZYGX2009J081]
- Scientific Research Foundation of Sichuan Province [2009JY0013]
Proteins belonging to different subfamilies of Voltage-gated K+ channels (VKC) are functionally divergent. The traditional method to classify ion channels is more time consuming. Thus, it is highly desirable to develop novel computational methods for VKC subfamily classification. In this study, a support vector machine based method was proposed to predict VKC subfamilies using amino acid and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife cross-validation, the proposed method (VKCPred) achieved an overall accuracy of 93.09% with 93.22% average sensitivity and 98.34% average specificity, which are superior to that of other two state-of-the-art classifiers. These results indicate that VKCPred can be efficiently used to identify and annotate voltage-gated K+ channels' subfamilies. The VKCPred software and dataset are freely available at http://cobi.uestc.edu.cn/people/hlin/tools/VKCPred/. (c) 2012 Elsevier Ltd. All rights reserved.
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