DocumentCode :
2122498
Title :
Three machine learning approaches in the epitope prediction
Author :
Wan, Yinan ; Liu, Yunfu ; Li, Tian ; Si, Shuping ; Xu, Cheng
Author_Institution :
Department of Bioinformatics, College of Life Science, Zhejiang University, Hangzhou, China
fYear :
2010
fDate :
4-6 Dec. 2010
Firstpage :
813
Lastpage :
817
Abstract :
B-cell epitopes are important in both fundamental biological research and the clinical application. Here we get through three most widely used machine learning approaches (Naïve Bayesian Classifier, Support Vector Machine and the Artificial Neural Network) in the epitope prediction of both continuous and discontinuous types. As the prediction for conformational epitopes are still new in the epitope prediction, feature selection is especially analyzed. Some comparisons and the discussion about the advantages and disadvantages are made about the three methods.
Keywords :
Accuracy; Artificial neural networks; Bayesian methods; Kernel; Machine learning; Proteins; Support vector machines; Artificial Neural Network; Naïve Bayesian Classifier; SVM; epitope prediction; machine learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering (ICISE), 2010 2nd International Conference on
Conference_Location :
Hangzhou, China
Print_ISBN :
978-1-4244-7616-9
Type :
conf
DOI :
10.1109/ICISE.2010.5690202
Filename :
5690202
Link To Document :
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