- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 794350, 9 pages
On the Variability of Neural Network Classification Measures in the Protein Secondary Structure Prediction Problem
Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA
Received 27 April 2012; Revised 19 November 2012; Accepted 27 November 2012
Academic Editor: Cheng-Jian Lin
Copyright © 2013 Eric Sakk and Ayanna Alexander. 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.
- C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
- M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing, 1996.
- M. N. Nguyen and J. C. Rajapakse, “Multi-class support vector machines for protein secondary structure prediction,” Genome Informatics, vol. 14, pp. 218–227, 2003.
- H. J. Hu, Y. Pan, R. Harrison, and P. C. Tai, “Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier,” IEEE Transactions on Nanobioscience, vol. 3, no. 4, pp. 265–271, 2004.
- W. Zhong, G. Altun, X. Tian, R. Harrison, P. C. Tai, and Y. Pan, “Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology,” Journal of Supercomputing, vol. 41, no. 1, pp. 1–16, 2007.
- J. A. Cuff and G. J. Barton, “Application of enhanced multiple sequence alignment profiles to improve protein secondary structure prediction,” Proteins, vol. 40, pp. 502–511, 2000.
- J. M. Chandonia and M. Karplus, “Neural networks for secondary structure and structural class predictions,” Protein Science, vol. 4, no. 2, pp. 275–285, 1995.
- J. A. Cuff and G. J. Barton, “Evaluation and improvement of multiple sequence methods for protein secondary structure prediction,” Proteins, vol. 34, pp. 508–519, 1999.
- G. E. Crooks and S. E. Brenner, “Protein secondary structure: entropy, correlations and prediction,” Bioinformatics, vol. 20, no. 10, pp. 1603–1611, 2004.
- L. H. Wang, J. Liu, and H. B. Zhou, “A comparison of two machine learning methods for protein secondary structure prediction,” in Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp. 2730–2735, chn, August 2004.
- G. Z. Zhang, D. S. Huang, Y. P. Zhu, and Y. X. Li, “Improving protein secondary structure prediction by using the residue conformational classes,” Pattern Recognition Letters, vol. 26, no. 15, pp. 2346–2352, 2005.
- N. Qian and T. J. Sejnowski, “Predicting the secondary structure of globular proteins using neural network models,” Journal of Molecular Biology, vol. 202, no. 4, pp. 865–884, 1988.
- L. Howard Holley and M. Karplus, “Protein secondary structure prediction with a neural network,” Proceedings of the National Academy of Sciences of the United States of America, vol. 86, no. 1, pp. 152–156, 1989.
- J. M. Chandonia and M. Karplus, “The importance of larger data sets for protein secondary structure prediction with neural networks,” Protein Science, vol. 5, no. 4, pp. 768–774, 1996.
- B. Rost and C. Sander, “Improved prediction of protein secondary structure by use of sequence profiles and neural networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 90, no. 16, pp. 7558–7562, 1993.
- B. Rost and C. Sander, “Prediction of protein secondary structure at better than 70% accuracy,” Journal of Molecular Biology, vol. 232, no. 2, pp. 584–599, 1993.
- J. A. Cuff, M. E. Clamp, A. S. Siddiqui, M. Finlay, and G. J. Barton, “JPred: a consensus secondary structure prediction server,” Bioinformatics, vol. 14, no. 10, pp. 892–893, 1998.
- S. Hua and Z. Sun, “A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach,” Journal of Molecular Biology, vol. 308, no. 2, pp. 397–407, 2001.
- B. Rost, “PHD: predicting one-dimensional protein structure by profile-based neural networks,” Methods in Enzymology, vol. 266, pp. 525–539, 1996.
- B. Rost, G. Yachdav, and J. Liu, “The PredictProtein server,” Nucleic Acids Research, vol. 32, pp. W321–W326, 2004.
- C. Cole, J. D. Barber, and G. J. Barton, “The Jpred 3 secondary structure prediction server,” Nucleic Acids Research, vol. 36, pp. W197–W201, 2008.
- G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, “Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles,” Proteins, vol. 47, no. 2, pp. 228–235, 2002.
- J. Cheng, A. Z. Randall, M. J. Sweredoski, and P. Baldi, “SCRATCH: a protein structure and structural feature prediction server,” Nucleic Acids Research, vol. 33, no. 2, pp. W72–W76, 2005.
- D. T. Jones, “Protein secondary structure prediction based on position-specific scoring matrices,” Journal of Molecular Biology, vol. 292, no. 2, pp. 195–202, 1999.
- K. Bryson, L. J. McGuffin, R. L. Marsden, J. J. Ward, J. S. Sodhi, and D. T. Jones, “Protein structure prediction servers at University College London,” Nucleic Acids Research, vol. 33, no. 2, pp. W36–W38, 2005.
- G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303–314, 1989.
- J. Moody and C. J. Darken, “Fast learning in networks of locally tuned processing units,” Neural Computation, vol. 1, pp. 281–294, 1989.
- T. Poggio and F. Girosi, “Networks for approximation and learning,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1481–1497, 1990.
- D. F. Specht, “Probabilistic neural networks,” Neural Networks, vol. 3, no. 1, pp. 109–118, 1990.
- P. András, “Orthogonal RBF neural network approximation,” Neural Processing Letters, vol. 9, no. 2, pp. 141–151, 1999.
- P. Baldi and S. Brunak, Bioinformatics: The Machine Learning Approach, MIT Press, 1998.
- E. Sakk, D. J. Schneider, C. R. Myers, and S. W. Cartinhour, “On the selection of target vectors for a class of supervised pattern recognizers,” IEEE Transactions on Neural Networks, vol. 20, no. 5, pp. 745–757, 2009.
- G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins University Press, 1989.