Protein Secondary Structure Prediction using an Optimised Bayesian Classification Neural Network

Son T. Nguyen, Colin G. Johnson

2013

Abstract

The prediction of protein secondary structure is a topic that has been tackled by many researchers in the field of bioinformatics. In previous work, this problem has been solved by various methods including the use of traditional classification neural networks with the standard error back-propagation training algorithm. Since the traditional neural network may have a poor generalisation, the Bayesian technique has been used to improve the generalisation and the robustness of these networks. This paper describes the use of optimised classification Bayesian neural networks for the prediction of protein secondary structure. The well-known RS126 dataset was used for network training and testing. The experimental results show that the optimised classification Bayesian neural network can reach an accuracy greater than 75%.

References

  1. B. Rost and C. Sander, "Prediction of protein secondary structure at better than 70% accuracy," J.Mol.Biol.,vol. 232, pp. 584-599, 1993a.
  2. B. Rost and C. Sander, "Improved prediction of protein secondary structure by use of sequence profiles and neural networks.," Proc, Natl, Acad, Sci, Biophysics, USA, pp. 7558 - 7562, 1993b.
  3. L. H. Holley and M. Karpus, "Protein secondary structure prediction with a neural network," Proc, Natl, Acad, Sci, Biophysics, USA, vol. 86, pp. 152 - 156, 1989.
  4. L. Lee, J. L. Leopold, and R. L. Frank, "Protein secondary structure prediction using BLAST and exhaustive RTRICO, the search for optimal segment length and threshold," 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 35 - 42, 2012.
  5. S. T. Nguyen, H. T. Nguyen, and P. Taylor, "Hands-Free Control of Power Wheelchairs using Bayesian Neural Networks," Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 2004, pp. 745 - 749, 2004.
  6. S. T. Nguyen, H.T.Nguyen, P. Taylor, and J. Middleton, "Improved Head Direction Command Classification using an Optimised Bayesian Neural Network," Proceedings of IEEE International Conference of the Engineering in Medicine and Biology Society, New York City, New York, USA, August 30-Sept. 3, 2006.
  7. W.D. Penny and S. J. Roberts, "Bayesian neural networks for classification: how useful is the evidence framework," Neural Networks, vol. 12, pp. 877 - 892, 1999.
  8. H. H. Thodberg, "A review of Bayesian neural networks with an application to near infrared spectroscopy," IEEE Transactions on Neural Networks, vol. 7, pp. 56 - 72, 1996.
  9. D. MacKay, "A practical Bayesian Framework for Backpropagation Networks," Computation and Neural Systems, vol. 4, pp. 448-472, 1992a.
  10. D. MacKay, "The Evidence Framework Applied to Classification Networks," Neural Computation, vol. 4, pp. 720 -736, 1992b.
  11. C. M. Bishop, "Neural networks for pattern recognition," Oxford: Clarendon Press; New York: Oxford University Press, 1995.
  12. M. A. Mottalib, M. S. R. Mahdi, A. B. M. Z. Haque, S. M. A. Mamun, and H. A. Al-Mamun, "Protein Secondary Structure Prediction using Feed-Forward Neural Network," JCIT, vol. 1, pp. 64 - 68, 2010.
  13. N. Qian and T. J. Sejnowski, "Predicting the Secondary Structure of Globular Proteins Using Neural Network Models," J. Mol. Biol. 202, pp. 865 - 884, 1988.
  14. M. F. Moller, "A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning," Neural Networks, vol. 6, pp. 525 - 533, 1993.
  15. Jpred 3, http://www.compbio.dundee.ac.uk/www-jpred/ David T. Jones, "Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices," J.Mol.Biol.,vol.292, pp.195-202, 1999.
  16. B. Sepideh, S. S. Ali, and G. A. R, "Pruning neural networks for protein secondary structure prediction," 8th IEEE International Conference on BioInformatics and BioEngineering, 2008. BIBE 2008, pp. 1 - 6, 2008.
  17. K. Rajasekhar, D. V. Kumar, and O. O. Ahmad, " A twostage neural network based technique for protein secondary structure prediction " The 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008, pp. 1355 - 1358 2008.
Download


Paper Citation


in Harvard Style

T. Nguyen S. and G. Johnson C. (2013). Protein Secondary Structure Prediction using an Optimised Bayesian Classification Neural Network . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 451-457. DOI: 10.5220/0004538604510457


in Bibtex Style

@conference{ncta13,
author={Son T. Nguyen and Colin G. Johnson},
title={Protein Secondary Structure Prediction using an Optimised Bayesian Classification Neural Network},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={451-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004538604510457},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Protein Secondary Structure Prediction using an Optimised Bayesian Classification Neural Network
SN - 978-989-8565-77-8
AU - T. Nguyen S.
AU - G. Johnson C.
PY - 2013
SP - 451
EP - 457
DO - 10.5220/0004538604510457