About this Journal Submit a Manuscript Table of Contents
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.

Linked References

  1. 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. View at Publisher · View at Google Scholar · View at Scopus
  2. 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. View at Publisher · View at Google Scholar · View at Scopus
  3. 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. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Honig, “Combining bioinformatics and biophysics to understand protein-protein and protein-ligand interactions,” The Scientific World Journal, vol. 2, pp. 43–44, 2002.
  5. G. Von Heijne, “Membrane-protein topology,” Nature Reviews Molecular Cell Biology, vol. 7, no. 12, pp. 909–918, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. 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.
  7. 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. View at Publisher · View at Google Scholar · View at Scopus
  8. 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. View at Publisher · View at Google Scholar · View at Scopus
  9. 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. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Persson and P. Argos, “Topology prediction of membrane proteins,” Protein Science, vol. 5, no. 2, pp. 363–371, 1996. View at Scopus
  11. 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. View at Publisher · View at Google Scholar · View at Scopus
  12. 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. View at Scopus
  13. 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. View at Publisher · View at Google Scholar · View at Scopus
  14. 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. View at Scopus
  15. 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. View at Scopus
  16. 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. View at Scopus
  17. 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. View at Scopus
  18. 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. View at Publisher · View at Google Scholar · View at Scopus
  19. 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. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Nugent and D. T. Jones, “Transmembrane protein topology prediction using support vector machines,” BMC Bioinformatics, vol. 26, no. 10, article 159, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. 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.
  22. 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. View at Scopus
  23. 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. View at Publisher · View at Google Scholar · View at Scopus
  24. 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. View at Scopus
  25. 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. View at Publisher · View at Google Scholar
  26. 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. View at Publisher · View at Google Scholar
  27. 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. View at Publisher · View at Google Scholar
  28. 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.
  29. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976.
  30. 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. View at Publisher · View at Google Scholar · View at Scopus
  31. 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. View at Publisher · View at Google Scholar · View at Scopus
  32. 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. View at Publisher · View at Google Scholar · View at Scopus
  33. 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. View at Publisher · View at Google Scholar · View at Scopus
  34. 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. View at Publisher · View at Google Scholar · View at Scopus
  35. 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.
  36. 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.
  37. B. Kang, Y. Deng, R. Sadiq, and S. Mahadevan, “Evidential cognitive maps,” Knowledge-Based Systems, vol. 35, pp. 77–86, 2012.
  38. 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. View at Publisher · View at Google Scholar · View at Scopus
  39. P. Smets and R. Kennes, “The transferable belief model,” Artificial Intelligence, vol. 66, no. 2, pp. 191–234, 1994. View at Scopus
  40. 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. View at Publisher · View at Google Scholar · View at Scopus
  41. 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. View at Publisher · View at Google Scholar · View at Scopus