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Prediction of protein structure classes with flexible neural tree

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Bao, Wenzheng; | Chen, Yuehui; | Wang, Dong

Affiliations: School of Information Science and Engineering, University of Jinan, Jinan, P.R. China

Note: [] Corresponding authors: Wenzheng Bao, School of Information Science and Engineering, University of Jinan, Xinzhuang West Road No. 336, Jinan, P.R. China. Tel.: +86-18353170677; Fax: +86-531-82767581; E-mail: [email protected]. Yuehui Chen, School of Information Science and Engineering, University of Jinan, Xinzhuang West Road No. 336, Jinan, P.R. China. Tel.: +86-531-82767581; Fax: +86-531-82767581; E-mail: [email protected].

Note: [] Corresponding authors: Wenzheng Bao, School of Information Science and Engineering, University of Jinan, Xinzhuang West Road No. 336, Jinan, P.R. China. Tel.: +86-18353170677; Fax: +86-531-82767581; E-mail: [email protected]. Yuehui Chen, School of Information Science and Engineering, University of Jinan, Xinzhuang West Road No. 336, Jinan, P.R. China. Tel.: +86-531-82767581; Fax: +86-531-82767581; E-mail: [email protected].

Keywords: Protein structural classification, flexible neural tree, correlation coefficient

DOI: 10.3233/BME-141209

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3797-3806, 2014

Published: 2014
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Abstract

Prediction of protein structural classes is of great significance to better understand protein folding patterns. An array of methods has been proposed to predict these structures based on sequences. However, the accuracy is strongly affected by the homology of sequences. In the present study, the features based on correlation coefficient of sequence and amino acid composition are extracted. Flexible neutral tree is employed as the classification model. To examine the performance of this method, four benchmark datasets are selected. Altogether, the results show that a higher prediction accuracy of alpha/beta can be achieved by the method compared to others.

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