Research article
Glycosylation site prediction using ensembles of Support Vector Machine classifiers
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* Corresponding author: Cornelia Caragea cornelia@cs.iastate.edu
1 Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, USA
2 Center for Computational Intelligence, Learning, and Discovery, Iowa State University, USA
3 Department of Genetics, Development and Cell Biology, Iowa State University, USA
4 Bioinformatics and Computational Biology Program, Iowa State University, USA
BMC Bioinformatics 2007, 8:438 doi:10.1186/1471-2105-8-438
Published: 9 November 2007Additional files
Additional file 1:
Comparison of single versus ensemble of Support Vector Machine classifiers using evolutionary information with Polynomial Kernel. ROC curves for single and ensemble of Support Vector Machine classifiers for N-, O-, and C-linked glycosylation using evolutionary information with Polynomial Kernel and the description of evolutionary information feature representation.
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Additional file 2:
Comparison of single versus ensemble of Naive Bayes classifiers and single Naive Bayes versus single SVM using local sequence information. ROC curves for single Naive Bayes and ensemble of Naive Bayes classifiers and ROC curves for single Naive Bayes and single SVM for N-, O-, and C-linked glycosylation using local sequence information.
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