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The Scientific World Journal
Volume 2013 (2013), Article ID 123731, 8 pages
http://dx.doi.org/10.1155/2013/123731
Research Article

TOPPER: Topology Prediction of Transmembrane Protein Based on Evidential Reasoning

1School of Computer and Information Science, Southwest University, Chongqing 400715, China
2School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
3Department of Biomedical Informatics, Medical Center, Vanderbilt University, Nashville, TN 37235, USA
4Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Sun Yat-sen University, Guangzhou 510006, China
5School of Engineering, Vanderbilt University, Nashville, TN 37235, USA

Received 28 September 2012; Accepted 18 October 2012

Academic Editors: S. Jahandideh and M. Liu

Copyright © 2013 Xinyang Deng 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 topology prediction of transmembrane protein is a hot research field in bioinformatics and molecular biology. It is a typical pattern recognition problem. Various prediction algorithms are developed to predict the transmembrane protein topology since the experimental techniques have been restricted by many stringent conditions. Usually, these individual prediction algorithms depend on various principles such as the hydrophobicity or charges of residues. In this paper, an evidential topology prediction method for transmembrane protein is proposed based on evidential reasoning, which is called TOPPER (topology prediction of transmembrane protein based on evidential reasoning). In the proposed method, the prediction results of multiple individual prediction algorithms can be transformed into BPAs (basic probability assignments) according to the confusion matrix. Then, the final prediction result can be obtained by the combination of each individual prediction base on Dempster’s rule of combination. The experimental results show that the proposed method is superior to the individual prediction algorithms, which illustrates the effectiveness of the proposed method.