Table of Contents Author Guidelines Submit a Manuscript
Journal of Biomedicine and Biotechnology
Volume 2011, Article ID 506205, 8 pages
http://dx.doi.org/10.1155/2011/506205
Research Article

Prediction of RNA-Binding Proteins by Voting Systems

1School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 2000721, China
2Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
3College of Life Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
4University of Shanghai for Science and Technology Library, 516 Jun-Gong Road, Shanghai 200093, China
5Department of Synthesis, WuXi AppTec Co., Ltd., Shanghai 200131, China
6Institute of Systems Biology, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China

Received 9 March 2011; Revised 12 May 2011; Accepted 26 May 2011

Academic Editor: Zoran Obradovic

Copyright © 2011 C. R. Peng 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

It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew’s correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier’s vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.