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

Subrata K Bose,Antony Browne,Kenneth White,Hassan Kazemian

doi:10.1186/1471-2105-6-s3-p3

Subrata K Bose, Antony Browne + Show 2 more

Open Access

https://doi.org/10.1186/1471-2105-6-s3-p3

Copy DOI

Abstract

proteinsNeural NetworksKnowledge DiscoverySecondary Structure Prediction There have been several att mpts over th 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.

Highlights

phlettmpe:/n/wt>wmcBeniotSrayls.Bcoiom: /Bcioonintefonrtm/padtfi/c1s4a7n1d-2S1y0s5te-6m-Ss3B-ionflo.gpydCfre Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here.
Though neural networks have been considered as classification and regression systems whose inner working principles were very difficult to interpret, it is 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
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

Summary

Introduction

phlettmpe:/n/wt>wmcBeniotSrayls.Bcoiom: /Bcioonintefonrtm/padtfi/c1s4a7n1d-2S1y0s5te-6m-Ss3B-ionflo.gpydCfre Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here.. Use Of Neural Networks To Predict And Analyse Membrane Subrata K Bose*2, Hassan Kazemian1, Kenneth White2 and Antony Browne3

Results

Conclusion