title: Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.
abstract: Voltage-gated K+ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs' subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems.
authors: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. lweixin316@gmail.com.
authors: Wei-Xin
authors: WX
authors: Liu
authors: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. enzeas@gmail.com.
authors: En-Ze
authors: EZ
authors: Deng
authors: Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China. greatchen@heuu.edu.cn.
authors: Wei
authors: W
authors: Chen
authors: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. hlin@uestc.edu.cn.
authors: Hao
authors: H
authors: Lin
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D020407:Internet; D009842:Oligopeptides / Q000737:chemistry / Q000378:metabolism; D024642:Potassium Channels, Voltage-Gated / Q000032:analysis*; D060388:Support Vector Machine; D014584:User-Computer Interface
chemical_list: D009842:Oligopeptides; D024642:Potassium Channels, Voltage-Gated