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
Link To Document :
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