A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation | Zendy

Qiwen Dong | Zendy; Shuigeng Zhou | Zendy; Jihong Guan | Zendy

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A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation

Author(s) -

Qiwen Dong,

Shuigeng Zhou,

Jihong Guan

Publication year - 2009

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/btp500

Subject(s) - support vector machine , computer science , classifier (uml) , pattern recognition (psychology) , transformation (genetics) , covariance , artificial intelligence , machine learning , data mining , mathematics , statistics , biology , biochemistry , gene

Fold recognition is an important step in protein structure and function prediction. Traditional sequence comparison methods fail to identify reliable homologies with low sequence identity, while the taxonomic methods are effective alternatives, but their prediction accuracies are around 70%, which are still relatively low for practical usage.

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