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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 492174, 8 pages
Research Article

Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

1Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China
2Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China
3School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey BT37 0QB, UK

Received 19 April 2012; Revised 9 September 2012; Accepted 9 September 2012

Academic Editor: George Perry

Copyright © 2012 Zhi Zheng 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.


A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.