research-article

Classification of Protein Structure Classes on Flexible Neutral Tree

Authors:

Yuehui ChenAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 14, Issue 5

Pages 1122 - 1133

https://doi.org/10.1109/TCBB.2016.2610967

Published: 01 September 2017 Publication History

Abstract

Accurate classification on protein structural is playing an important role in Bioinformatics. An increase in evidence demonstrates that a variety of classification methods have been employed in such a field. In this research, the features of amino acids composition, secondary structure's feature, and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree FNT, a particular tree structure neutral network, has been employed as the classification model in the protein structures’ classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling IFS algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189, and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate that the performance of the proposed method is better than the other methods in the low-homology protein tertiary structures.

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 14, Issue 5

September 2017

202 pages

ISSN:1545-5963

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Copyright © 2017.

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 September 2017

Published in TCBB Volume 14, Issue 5

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