- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
The Scientific World Journal
Volume 2013 (2013), Article ID 123731, 8 pages
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.
- A. Krogh, B. Larsson, G. Von Heijne, and E. L. L. Sonnhammer, “Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes,” Journal of Molecular Biology, vol. 305, no. 3, pp. 567–580, 2001.
- G. Von Heijne, “Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule,” Journal of Molecular Biology, vol. 225, no. 2, pp. 487–494, 1992.
- H. Viklund and A. Elofsson, “OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar,” Bioinformatics, vol. 24, no. 15, pp. 1662–1668, 2008.
- B. Honig, “Combining bioinformatics and biophysics to understand protein-protein and protein-ligand interactions,” The Scientific World Journal, vol. 2, pp. 43–44, 2002.
- G. Von Heijne, “Membrane-protein topology,” Nature Reviews Molecular Cell Biology, vol. 7, no. 12, pp. 909–918, 2006.
- L.-P. Tian, L.-Z. Liu, Q.-W. Zhang, and F.-X. Wu, “Nonlinear model-based method for clustering periodically expressed genes,” The Scientific World Journal, vol. 11, pp. 2051–2061, 2011.
- A. J. Lightfoot, H. M. Rosevear, and M. A. O'Donnell, “Recognition and treatment of BCG failure in bladder cancer,” The Scientific World Journal, vol. 11, pp. 602–613, 2011.
- B. Ercole and D. J. Parekh, “Methods to predict and lower the risk of prostate cancer,” The Scientific World Journal, vol. 11, pp. 742–748, 2011.
- K. Melén, A. Krogh, and G. Von Heijne, “Reliability measures for membrane protein topology prediction algorithms,” Journal of Molecular Biology, vol. 327, no. 3, pp. 735–744, 2003.
- B. Persson and P. Argos, “Topology prediction of membrane proteins,” Protein Science, vol. 5, no. 2, pp. 363–371, 1996.
- G. E. Tusnády and I. Simon, “Principles governing amino acid composition of integral membrane proteins: application to topology prediction,” Journal of Molecular Biology, vol. 283, no. 2, pp. 489–506, 1998.
- J. Kyte and R. F. Doolittle, “A simple method for displaying the hydropathic character of a protein,” Journal of Molecular Biology, vol. 157, no. 1, pp. 105–132, 1982.
- A. Bernsel, H. Viklund, J. Falk, E. Lindahl, G. Von Heijne, and A. Elofsson, “Prediction of membrane-protein topology from first principles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 20, pp. 7177–7181, 2008.
- D. T. Jones, W. Taylor, and J. Thornton, “A model recognition approach to the prediction of all-helical membrane protein structure and topology,” Biochemistry, vol. 33, no. 10, pp. 3038–3049, 1994.
- C. Pasquier, V. J. Promponas, G. A. Palaios, J. S. Hamodrakas, and S. J. Hamodrakas, “A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm,” Protein Engineering, vol. 12, no. 5, pp. 381–385, 1999.
- B. Rost, R. Casadio, P. Fariselli, and C. Sander, “Transmembrane helices predicted at 95% accuracy,” Protein Science, vol. 4, no. 3, pp. 521–533, 1995.
- B. Rost, R. Casadio, and P. Fariselli, “Refining neural network predictions for helical transmembrane proteins by dynamic programming,” Proceedings of the International Conference on Intelligent Systems for Molecular Biology, vol. 4, pp. 192–200, 1996.
- Q. Liu, Y. S. Zhu, B. H. Wang, and Y. X. Li, “A HMM-based method to predict the transmembrane regions of β-barrel membrane proteins,” Computational Biology and Chemistry, vol. 27, no. 1, pp. 69–76, 2003.
- Y. Deng, Q. Liu, and Y. X. Li, “Scoring hidden Markov models to discriminate β-barrel membrane proteins,” Computational Biology and Chemistry, vol. 28, no. 3, pp. 189–194, 2004.
- T. Nugent and D. T. Jones, “Transmembrane protein topology prediction using support vector machines,” BMC Bioinformatics, vol. 26, no. 10, article 159, 2009.
- J. Wang, Y. Li, Q. Wang et al., “Pro- ClusEnsem: predicting membrane protein types by fusing different modes of pseudo amino acid composition,” Computers in Biology and Medicine, vol. 42, no. 5, pp. 564–574, 2012.
- J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, “On combining classifiers,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226–239, 1998.
- L. Xu, A. Krzyzak, and C. Y. Suen, “Methods of combining multiple classifiers and their applications to handwriting recognition,” IEEE Transactions on Systems, Man and Cybernetics, vol. 22, no. 3, pp. 418–435, 1992.
- W. Wong, P. J. Fos, and F. E. Petry, “Combining the performance strengths of the logistic regression and neural network models: a medical outcomes approach,” The Scientific World Journal, vol. 3, pp. 455–476, 2003.
- K. Kusonmano, M. Netzer, C. Baumgartner, M. Dehmer, K. R. Liedl, and A. Graber, “Effects of pooling samples on the performance of classification algorithms: a comparative study,” The Scientific World Journal, vol. 2012, Article ID 278352, 10 pages, 2012.
- A. M. Barbosa and R. Real, “Applying fuzzy logic to comparative distri- bution modelling: a case study with two sympatric amphibians,” The Scientific World Journal, vol. 2012, Article ID 428206, 10 pages, 2012.
- H. Al-Mubaid and S. Gungu, “A learning-based approach for biomedical word sense disambiguation,” The Scientific World Journal, vol. 2012, Article ID 949247, 8 pages, 2012.
- A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” Annals of Mathematics and Statistics, vol. 38, no. 2, pp. 325–339, 1967.
- G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976.
- Y. Deng, R. Sadiq, W. Jiang, and S. Tesfamariam, “Risk analysis in a linguistic environment: a fuzzy evidential reasoning-based approach,” Expert Systems with Applications, vol. 38, no. 12, pp. 15438–15446, 2011.
- D. Yong, S. WenKang, Z. ZhenFu, and L. Qi, “Combining belief functions based on distance of evidence,” Decision Support Systems, vol. 38, no. 3, pp. 489–493, 2004.
- Y. Deng and F. T. S. Chan, “A new fuzzy dempster MCDM method and its application in supplier selection,” Expert Systems with Applications, vol. 38, no. 8, pp. 9854–9861, 2011.
- Y. Deng, F. T. S. Chan, Y. Wu, and D. Wang, “A new linguistic MCDM method based on multiple-criterion data fusion,” Expert Systems with Applications, vol. 38, no. 6, pp. 6985–6993, 2011.
- Y. Deng, W. Jiang, and R. Sadiq, “Modeling contaminant intrusion in water distribution networks: a new similarity-based DST method,” Expert Systems with Applications, vol. 38, no. 1, pp. 571–578, 2011.
- Y. Deng, Y. Chen, Y. Zhang, and S. Mahadevan, “Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment,” Applied Soft Computing, vol. 12, no. 3, pp. 1231–1237, 2012.
- Y. Zhang, X. Deng, D. Wei, and Y. Deng, “Assessment of E-Commerce security using AHP and evidential reasoning,” Expert Systems with Applications, vol. 39, no. 3, pp. 3611–3623, 2012.
- B. Kang, Y. Deng, R. Sadiq, and S. Mahadevan, “Evidential cognitive maps,” Knowledge-Based Systems, vol. 35, pp. 77–86, 2012.
- H. Viklund and A. Elofsson, “Best α-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information,” Protein Science, vol. 13, no. 7, pp. 1908–1917, 2004.
- P. Smets and R. Kennes, “The transferable belief model,” Artificial Intelligence, vol. 66, no. 2, pp. 191–234, 1994.
- S. Jayasinghe, K. Hristova, and S. H. White, “MPtopo: a database of membrane protein topology,” Protein Science, vol. 10, no. 2, pp. 455–458, 2001.
- W. Li and A. Godzik, “Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences,” Bioinformatics, vol. 22, no. 13, pp. 1658–1659, 2006.