DocumentCode
2126667
Title
Application Research of Protein Structure Prediction Based Support Vector Machine
Author
Wang, Bo ; Liu, Yongkui ; Yun, Jian ; Liu, Shuang
Author_Institution
Coll. of Comput. Sci. & Eng., Dalian Nat. Univ., Dalian
fYear
2008
fDate
21-22 Dec. 2008
Firstpage
581
Lastpage
584
Abstract
Bioinformatics techniques to protein structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. Support vector machines is a good classifier to solve classification problem and the learning results possess stronger robustness. We summarise some of the recent studies adopting this SVM learning machine for prediction structure prediction are the one which used frequent profiles with evolutionary information.
Keywords
bioinformatics; generalisation (artificial intelligence); learning (artificial intelligence); molecular biophysics; pattern classification; proteins; support vector machines; SVM generalization ability; amino acid sequence; bioinformatics technique; pattern classification problem; protein structure prediction; support vector machine learning; Amino acids; Application software; Bioinformatics; Computational biology; Knowledge acquisition; Machine learning; Protein engineering; Sequences; Support vector machine classification; Support vector machines; Bioinformatics; Protein Structure Prediction; Support Vector Machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3488-6
Type
conf
DOI
10.1109/KAM.2008.115
Filename
4732892
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