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BioMed Research International
Volume 2017 (2017), Article ID 2929807, 8 pages
https://doi.org/10.1155/2017/2929807
Research Article

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China

Correspondence should be addressed to Wang Xianfang; moc.361@gnafgnaw2

Received 29 December 2016; Revised 22 February 2017; Accepted 19 March 2017; Published 9 April 2017

Academic Editor: Loris Nanni

Copyright © 2017 Wang Xianfang 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.

Abstract

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the value is used to measure the significance level of the feature for the result, and the attribute with smaller value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.