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This article is part of the supplement: BioSysBio: Bioinformatics and Systems Biology Conference

Open Access Poster presentation

Use Of Neural Networks To Predict And Analyse Membrane Proteins In The Proteome

Subrata K Bose2*, Hassan Kazemian1, Kenneth White2 and Antony Browne3

Author Affiliations

1 Department of Computing, Communication Technology and Mathematics, London Metropolitan University, London, UK

2 Institute for Health Research and Policy, London Metropolitan University, London, UK

3 Department of Computing, University of Surrey, UK

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BMC Bioinformatics 2005, 6(Suppl 3):P3  doi:10.1186/1471-2105-6-S3-P3

The electronic version of this article is the complete one and can be found online at:

Published:21 September 2005


proteins; Neural Networks; Knowledge Discovery; Secondary Structure Prediction

Poster presentation

There have been several attempts over the last 20 years to develop tools for predicting membrane-spanning regions, but the problem of prediction is made topologically more complex by the presence of several transmembrane domains in many proteins, and current tools are far away from achieving 95% reliability in prediction. Though neural networks have been considered as classification and regression systems whose inner working principles were very difficult to interpret, it is now becoming apparent that algorithms can be designed which extract understandable representations from trained neural networks that might be a powerful tool for biological data mining. In this research construction of novel neural network architectures/algorithms, amino acid representations to the neural networks with appropriate encodings and understanding of the relationship between structure and function of transmembrane proteins were studied.

This work seeks to develop the use of artificial neural networks for analysing primary sequences for the presence of MSRs and to attempt classification according to functional and /or structural properties. This could be achieved by developing techniques for analysing primary protein sequences for the presence of membrane spanning regions using artificial neural network approaches. The expected benefits include an increased understanding of how to create and train optimal neural networks for membrane protein datasets, which will be extremely useful in both academia and industry. In addition, novel neural network architectures will be generated, leading to an enhancement of understanding of these machine-learning techniques.