NN approach and its comparison with NN-SVM to beta-barrel prediction

Kazemian, Hassan and Yusuf, S. A. and White, Kenneth and Grimaldi, Cedric Maxime (2016) NN approach and its comparison with NN-SVM to beta-barrel prediction. Expert Systems With Applications, 61. pp. 203-214.

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Abstract

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions.

Item Type: Article
Uncontrolled Keywords: Beta barrel prediction, Protein analysis, Neural Networks, Support vector machine
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Hassan Kazemian
Date Deposited: 03 May 2017 15:43
Last Modified: 03 May 2017 15:43
URI: http://repository.londonmet.ac.uk/id/eprint/1209

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