A new protein structure classification model

Author

Dong Wang ; Shiyuan Han ; Yuehui Chen ; Wenzheng Bao ; Kun Ma ; Abraham, Ajith

Author_Institution

Sch. of Inf. Sci. & Eng., Univ. of Jinan, Jinan, China

fYear

2014

fDate

July 30 2014-Aug. 1 2014

Firstpage

37

Lastpage

42

Abstract

Protein structure prediction is an important area of research in bioinformatics. In this paper, we select the features of correlation coefficient sequence and special amino acid composition. The support vector machine and a particular framework of ECOC are employed as classification model. To evaluate the efficiency of the proposed method we choose three benchmark protein sequence datasets (25PDB, 40PDB and ASTRAL) as the test dataset. The final results show that our method is efficient for protein structure prediction.

Keywords

bioinformatics; feature selection; molecular configurations; pattern classification; proteins; support vector machines; ECOC; SVM; amino acid composition; bioinformatics; correlation coefficient sequence; feature selection; protein sequence datasets; protein structure classification model; protein structure prediction; support vector machine; Bayes methods; Classification algorithms; Proteins; ECOC; Prediction structure of protein; Support Vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Aspects of Social Networks (CASoN), 2014 6th International Conference on

Conference_Location

Porto

Print_ISBN

978-1-4799-5939-6

Type

conf

DOI

10.1109/CASoN.2014.6920419

Filename

6920419