An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier | Zendy

Jiaqi Xia | Zendy; Zhenling Peng | Zendy; Dawei Qi | Zendy; Hongbo Mu | Zendy; Jianyi Yang | Zendy

Open Access

An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier

Author(s) -

Jiaqi Xia,

Zhenling Peng,

Dawei Qi,

Hongbo Mu,

Jianyi Yang

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw768

Subject(s) - threading (protein sequence) , support vector machine , fold (higher order function) , artificial intelligence , classifier (uml) , computer science , protein structure prediction , ab initio , benchmark (surveying) , pattern recognition (psychology) , machine learning , protein structure , algorithm , chemistry , biochemistry , organic chemistry , geodesy , programming language , geography

Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learning algorithms. Combination of both solutions to improve the prediction accuracy was never explored before.

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