Campus Units

Computer Science, Genetics, Development and Cell Biology

Document Type

Conference Proceeding

Conference

IEEE International Conference on Bioinformatics and Biomedicine

Publication Version

Accepted Manuscript

Publication Date

2008

First Page

289

Last Page

292

DOI

10.1109/BIBM.2008.80

Conference Title

2008 IEEE International Conference on Bioinformatics and Biomedicine

Conference Date

November 3-5, 2008

City

Philadelphia, Pennsylvania

Abstract

Mapping B-cell epitopes plays an important role in vaccine design, immunodiagnostic tests, and antibody production. Because the experimental determination of B-cell epitopes is time-consuming and expensive, there is an urgent need for computational methods for reliable identification of putative B-cell epitopes from antigenic sequences. In this study, we explore the utility of evolutionary profiles derived from antigenic sequences in improving the performance of machine learning methods for protective linear B-cell epitope prediction. Specifically, we compare propensity scale based methods with a Naive Bayes classifier using three different representations of the classifier input: amino acid identities, position specific scoring matrix (PSSM) profiles, and dipeptide composition. We find that in predicting protective linear B-cell epitopes, a Naive Bayes classifier trained using PSSM profiles significantly outperforms the propensity scale based methods as well as the Naive Bayes classifiers trained using the amino acid identity or dipeptide composition representations of input data.

Comments

This is a proceeding from IEEE International Conference on Bioinformatics and Biomedicine (2008): 289, doi: 10.1109/BIBM.2008.80. Posted with permission.

Rights

© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

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