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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 492174, 8 pages
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.
- K.-C. Chou, “Prediction of protein signal sequences,” Current Protein and Peptide Science, vol. 3, no. 6, pp. 615–622, 2002.
- K.-C. Chou, “Structural bioinformatics and its impact to biomedical science,” Current Medicinal Chemistry, vol. 11, no. 16, pp. 2105–2134, 2004.
- G. Lubec, L. Afjehi-Sadat, J. W. Yang, and J. P. P. John, “Searching for hypothetical proteins: theory and practice based upon original data and literature,” Progress in Neurobiology, vol. 77, no. 1-2, pp. 90–127, 2005.
- H.-B. Shen and K.-C. Chou, “Signal-3L: a 3-layer approach for predicting signal peptides,” Biochemical and Biophysical Research Communications, vol. 363, no. 2, pp. 297–303, 2007.
- K.-C. Chou and H.-B. Shen, “Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides,” Biochemical and Biophysical Research Communications, vol. 357, no. 3, pp. 633–640, 2007.
- K.-C. Chou, “Prediction of signal peptides using scaled window,” Peptides, vol. 22, no. 12, pp. 1973–1979, 2001.
- K.-C. Chou, “Using subsite coupling to predict signal peptides,” Protein Engineering, vol. 14, no. 2, pp. 75–79, 2001.
- G. Schneider, S. Rohlk, and P. Wrede, “Analysis of cleavage-site patterns in protein precursor sequences with a perceptron-type neural network,” Biochemical and Biophysical Research Communications, vol. 194, no. 2, pp. 951–959, 1993.
- H. Nielsen, J. Engelbrecht, S. Brunak, and G. von Heijne, “A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites,” International Journal of Neural Systems, vol. 8, no. 5-6, pp. 581–599, 1997.
- J. D. Bendtsen, H. Nielsen, G. Von Heijne, and S. Brunak, “Improved prediction of signal peptides: signalP 3.0,” Journal of Molecular Biology, vol. 340, no. 4, pp. 783–795, 2004.
- D. Plewczynski, L. Slabinski, K. Ginalski, and L. Rychlewski, “Prediction of signal peptides in protein sequences by neural networks,” Acta Biochimica Polonica, vol. 55, no. 2, pp. 261–267, 2008.
- H. Nielsen and A. Krogh, “Prediction of signal peptides and signal anchors by a hidden Markov model,” Intelligent Systems for Molecular Biology, vol. 1, no. 6, pp. 122–130, 1998.
- J. P. Vert, “Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings,” in Proceedings of Pacific Symposium on Biocomputing, Kauai, Hawaii, USA, 2002.
- Y.-D. Cai, S.-L. Lin, and K.-C. Chou, “Support vector machines for prediction of protein signal sequences and their cleavage sites,” Peptides, vol. 24, no. 1, pp. 159–161, 2003.
- C. Chen, X. Zhou, Y. Tian, X. Zou, and P. Cai, “Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network,” Analytical Biochemistry, vol. 357, no. 1, pp. 116–121, 2006.
- P. G. Bagos, K. D. Tsirigos, S. K. Plessas, T. D. Liakopoulos, and S. J. Hamodrakas, “Prediction of signal peptides in archaea,” Protein Engineering, Design and Selection, vol. 22, no. 1, pp. 27–35, 2009.
- P. P. Łabaj, G. G. Leparc, A. F. Bardet, G. Kreil, and D. P. Kreil, “Single amino acid repeats in signal peptides,” The FEBS Journal, vol. 277, no. 15, pp. 3147–3157, 2010.
- J. Pearl, “Bayesian networks: a model of self-activated memory for evidential reasoning,” in Proceedings of the 7th Annual Conference of the Cognitive Science Society, Computer Science Department, University of California, Los Angeles, Calif, USA, 1985.
- N. Friedman, D. Geiger, and M. Goldszmidt, “Bayesian network classifiers,” Machine Learning, vol. 29, no. 2-3, pp. 131–163, 1997.
- R. E. Neapolitan, Learning Bayesian Networks, Prentice Hall Series in Artificial Intelligence, Chicago, Ill, USA, 2004.
- I. Ben-Gal, A. Shani, A. Gohr et al., “Identification of transcription factor binding sites with variable-order Bayesian networks,” Bioinformatics, vol. 21, no. 11, pp. 2657–2666, 2005.
- K. Wang, J. Zhang, F. Shen, and L. Shi, “Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series,” Knowledge and Information Systems, vol. 17, no. 1, pp. 121–133, 2008.
- L. Sang, Y. Yang, Z. Wu, and W. Zhang, “Dynamic Bayesian network approach to speaker identification,” Electronics Letters, vol. 39, no. 3, pp. 329–330, 2003.
- H. Liu, J. Yang, J. G. Ling, and K.-C. Chou, “Prediction of protein signal sequences and their cleavage sites by statistical rulers,” Biochemical and Biophysical Research Communications, vol. 338, no. 2, pp. 1005–1011, 2005.
- K. Hiller, A. Grote, M. Scheer, R. Münch, and D. Jahn, “PrediSi: prediction of signal peptides and their cleavage positions,” Nucleic Acids Research, vol. 32, pp. W375–W379, 2004.
- J.-Y. Wang, Application of Support Vector Machines in Bioinformatics, National Taiwan University, 2002.
- D. Grossman and P. Domingos, “Learning Bayesian Network classifiers by maximizing conditional likelihood,” in Proceedings of the 21st International Conference on Machine Learning (ICML '04), pp. 361–368, Department of Computer Science and Engineering, University of Washington, Seattle, Wash, USA, July 2004.
- G. F. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Machine Learning, vol. 9, no. 4, pp. 309–347, 1992.
- R. R. Bouckaert, Bayesian Network Classifiers in Weka, University of Waikato, Hamilton, New Zealand, 2004.
- K. P. Murphy, “The Bayes Net Toolbox for Matlab,” http://people.cs.ubc.ca/~murphyk/Papers/bnt.pdf.
- J. Lin, Y. Wang, and X. Xu, “A novel ensemble and composite approach for classifying proteins based on Chou's pseudo amino acid composition,” African Journal of Biotechnology, vol. 10, no. 74, pp. 16963–16968, 2011.
- Y. Lu, “Knowledge integration in a multiple classifier system,” Applied Intelligence, vol. 6, no. 2, pp. 75–86, 1996.