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Protein Secondary Structure Prediction using an Optimised Bayesian Classification Neural Network

Nguyen, Son T., Johnson, Colin G. (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. . pp. 451-457. Science and Technology Publications, Setúbal, Portugal ISBN 978-989-8565-77-8. (doi:10.5220/0004538604510457)

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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%.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.5220/0004538604510457
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Q Science > QH Natural history > QH324.2 Computational biology
Divisions: Faculties > Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 13 Dec 2018 15:34 UTC
Last Modified: 30 May 2019 08:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/71007 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
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