Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Cornelia Caragea¹^,2 , Jivko Sinapov¹^,2 , Adrian Silvescu¹^,2 , Drena Dobbs³^,4 and Vasant Honavar¹^,2

¹Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, USA

²Center for Computational Intelligence, Learning, and Discovery, Iowa State University, USA

³Department of Genetics, Development and Cell Biology, Iowa State University, USA

⁴Bioinformatics and Computational Biology Program, Iowa State University, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:438doi:10.1186/1471-2105-8-438


Published:	9 November 2007

Additional 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.

Format: PDF Size: 87KB Download file

This file can be viewed with: Adobe Acrobat Reader

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.

Format: PDF Size: 86KB Download file

This file can be viewed with: Adobe Acrobat Reader