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%.

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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