Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Program Computational Intelligence, Learning, and Discovery Program

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<ul><li><p>Artificial Intelligence Research LaboratoryBioinformatics and Computational Biology ProgramComputational Intelligence, Learning, and Discovery ProgramDepartment of Computer ScienceRECOMB 2007</p><p>Acknowledgements: This work is supported in part by a grant from the National Institutes of Health (GM 066387) to Vasant Honavar &amp; Drena Dobbs</p><p>Glycosylation Site Prediction using Machine Learning Approaches </p><p>Cornelia Caragea, Jivko Sinapov, Adrian Silvescu, Drena Dobbs and Vasant Honavar Biological MotivationGlycosylation is one of the most complex post-translational modifications (PTMs). It is the site-specific enzymatic addition of saccharides to proteins and lipids. Most proteins in eukaryotic cells undergo glycosylation.Types of GlycosylationMKLITILCFLSRLLPSLTQESSQEIDNon O-Glycosylated?O-Glycosylated?H3N+COO-Problem: Predict glycosylation sites from amino acid sequencePrevious Approaches Trained Neural Networks used in netOglyc prediction server (Hansen et al., 1995) Dataset: mucin type O-linked glycosylation sites in mammalian proteins Trained SVMs based on physical properties, 0/1 system and a combination of these two (Li et al., 2006) Dataset: mucin type O-linked glycosylation sites in mammalian proteins Negative examples extracted from sequences with no known glycosylated sites Trained/tested using different ratios of positive and negative sites Our Approach We investigate 3 types of glycosylation and use an ensemble classifier approach Dataset: N-, C- and O-linked glycoslation sites in proteins from several different species: human, rat, mouse, insect, worm, horse, etc. Negative examples extracted from sequences with at least one experimentally verified glycosylated site</p><p>DatasetO-GlycBase v6.00: O- , N- &amp; C- glycosylated proteins with 242 glycosylated entries available at http://www.cbs.dtu.dk/databases/OGLYCBASE/Oglyc.base.htmlTraining an ensemble classifierClassifiers SVM 0/1 String Kernel</p><p> Substitution Matrix Kernel</p><p> PSI-Blast PSSM - Polynomial Kernel Decision Tree Nave Bayes Identity windows Identity plus additional informationROC Curves for N-Linked ROC Curves for O-Linked ROC Curves for C-Linked Comparison of ROC Curves for single and ensemble classifier ResultsConclusionIn this work we addressed the problem of predicting O-, N-, and C-Linked glycosylation sites from protein sequences. We trained and evaluated an Ensemble Classifier in conjunction with SVM, Nave Bayes and Decision Tree models. Our experiments show that an ensemble classifier approach achieves low generalization error and can outperform a single trained classifier. </p><p>Glycosylation TypePositive SitesNegative SitesO-Linked (S/T)209811623N-Linked (N)2511430C-Linked (W)4773Total236613126</p></li></ul>

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