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Machine learning approaches for epit...

Iowa State University., Computer Science.

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Machine learning approaches for epitope prediction.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning approaches for epitope prediction./
Author:	EL-Manzalawy, Yasser.
Description:	179 p.
Notes:	Adviser: Vasant Honavar.
Contained By:	Dissertation Abstracts International70-01B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3342247
ISBN:	9780549996859

Machine learning approaches for epitope prediction.
EL-Manzalawy, Yasser.

Machine learning approaches for epitope prediction. - 179 p.

Adviser: Vasant Honavar.

Thesis (Ph.D.)--Iowa State University, 2008.

The identification and characterization of epitopes in antigenic sequences is critical for understanding disease pathogenesis, for identifying potential autoantigens, and for designing vaccines and immune-based cancer therapies. As the number of pathogen genomes fully or partially sequenced is rapidly increasing, experimental methods for epitope mapping would be prohibitive in terms of time and expenses. Therefore, computational methods for reliably identifying potential vaccine candidates (i.e., epitopes that invoke strong response from both T-cells and B-cells) are highly desirable.

ISBN: 9780549996859Subjects--Topical Terms:

769149
Artificial Intelligence.

Machine learning approaches for epitope prediction.
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020 $a 9780549996859
035 $a (UMI)AAI3342247
035 $a AAI3342247
040 $a UMI $c UMI
100 1 $a EL-Manzalawy, Yasser. $3 1023884
245 1 0 $a Machine learning approaches for epitope prediction.
300 $a 179 p.
500 $a Adviser: Vasant Honavar.
500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0392.
502 $a Thesis (Ph.D.)--Iowa State University, 2008.
520 $a The identification and characterization of epitopes in antigenic sequences is critical for understanding disease pathogenesis, for identifying potential autoantigens, and for designing vaccines and immune-based cancer therapies. As the number of pathogen genomes fully or partially sequenced is rapidly increasing, experimental methods for epitope mapping would be prohibitive in terms of time and expenses. Therefore, computational methods for reliably identifying potential vaccine candidates (i.e., epitopes that invoke strong response from both T-cells and B-cells) are highly desirable.
520 $a Machine learning offers one of the most cost-effective and widely used approaches to developing epitope prediction tools. In the last few years, several advances in machine learning research have emerged. We utilize recent advances in machine learning research to provide epitope prediction tools with improved predictive performance. First, we introduce two methods, BCPred and FBCPred, for predicting linear B-cell epitopes and flexible length linear B-cell epitopes, respectively, using string kernel based support vector machine (SVM) classifiers. Second, we introduce three scoring matrix methods and show that they are highly competitive with a broad class of machine learning methods, including SVM, in predicting major histocompatibility complex class I (MHC-I) binding peptides. Finally, we formulate the problems of qualitatively and quantitatively predicting exible length major histocompatibility complex class II (MHC-II) peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression.
520 $a The development of reliable epitope prediction tools is not feasible in the absence of high quality data sets. Unfortunately, most of the existing epitope benchmark data sets are comprised of epitope sequences that share high degree of similarity with other peptide sequences in the same data set. We demonstrate the pitfalls of these commonly used data sets for evaluating the performance of machine learning approaches to epitope prediction. Finally, we propose a similarity reduction procedure that is more stringent than currently used similarity reduction methods.
590 $a School code: 0097.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0800
690 $a 0984
710 2 $a Iowa State University. $b Computer Science. $3 1022280
773 0 $t Dissertation Abstracts International $g 70-01B.
790 $a 0097
790 1 0 $a Dobbs, Drena $e committee member
790 1 0 $a Fernandez-Baca, David $e committee member
790 1 0 $a Honavar, Vasant, $e advisor
790 1 0 $a Kamal, Ahmed $e committee member
790 1 0 $a Margaritis, Dimitris $e committee member
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3342247