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

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 seque...

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Main Authors:	Zhi Zheng, Youying Chen, Liping Chen, Gongde Guo, Yongxian Fan, Xiangzeng Kong
Format:	Article
Language:	English
Published:	Hindawi Limited 2012-01-01
Series:	Journal of Biomedicine and Biotechnology
Online Access:	http://dx.doi.org/10.1155/2012/492174

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spelling	doaj-bf89a7ffd036444cb397a3b4a643876f2020-11-25T00:49:56ZengHindawi LimitedJournal of Biomedicine and Biotechnology1110-72431110-72512012-01-01201210.1155/2012/492174492174Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal PeptidesZhi Zheng0Youying Chen1Liping Chen2Gongde Guo3Yongxian Fan4Xiangzeng Kong5Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, ChinaKey Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, ChinaKey Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, ChinaKey Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, ChinaKey Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, ChinaA 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.http://dx.doi.org/10.1155/2012/492174
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Zhi Zheng Youying Chen Liping Chen Gongde Guo Yongxian Fan Xiangzeng Kong
spellingShingle	Zhi Zheng Youying Chen Liping Chen Gongde Guo Yongxian Fan Xiangzeng Kong Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides Journal of Biomedicine and Biotechnology
author_facet	Zhi Zheng Youying Chen Liping Chen Gongde Guo Yongxian Fan Xiangzeng Kong
author_sort	Zhi Zheng
title	Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides
title_short	Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides
title_full	Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides
title_fullStr	Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides
title_full_unstemmed	Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides
title_sort	signal-bnf: a bayesian network fusing approach to predict signal peptides
publisher	Hindawi Limited
series	Journal of Biomedicine and Biotechnology
issn	1110-7243 1110-7251
publishDate	2012-01-01
description	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.
url	http://dx.doi.org/10.1155/2012/492174
work_keys_str_mv	AT zhizheng signalbnfabayesiannetworkfusingapproachtopredictsignalpeptides AT youyingchen signalbnfabayesiannetworkfusingapproachtopredictsignalpeptides AT lipingchen signalbnfabayesiannetworkfusingapproachtopredictsignalpeptides AT gongdeguo signalbnfabayesiannetworkfusingapproachtopredictsignalpeptides AT yongxianfan signalbnfabayesiannetworkfusingapproachtopredictsignalpeptides AT xiangzengkong signalbnfabayesiannetworkfusingapproachtopredictsignalpeptides
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Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

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