Building classifier ensembles for B-cell epitope prediction

Yasser El-Manzalawy, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)

Abstract

Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or conformational, i.e., amino acids that are often not contiguous in the primary sequence but appear in close proximity within the folded 3D antigen structure. Numerous computational methods have been proposed for predicting both types of B-cell epitopes. However, the development of tools for reliably predicting B-cell epitopes remains a major challenge in immunoinformatics. Classifier ensembles a promising approach for combining a set of classifiers such that the overall performance of the resulting ensemble is better than the predictive performance of the best individual classifier. In this chapter, we show how to build a classifier ensemble for improved prediction of linear B-cell epitopes. The method can be easily adapted to build classifier ensembles for predicting conformational epitopes.

Original languageEnglish (US)
Title of host publicationImmunoinformatics
PublisherHumana Press Inc.
Pages285-294
Number of pages10
ISBN (Print)9781493911141
DOIs
StatePublished - 2014

Publication series

NameMethods in Molecular Biology
Volume1184
ISSN (Print)1064-3745

Fingerprint

B-Lymphocyte Epitopes
Antigens
Epitopes
Antibody Formation
Amino Acid Sequence
Vaccines
Amino Acids

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

Cite this

El-Manzalawy, Y., & Honavar, V. (2014). Building classifier ensembles for B-cell epitope prediction. In Immunoinformatics (pp. 285-294). (Methods in Molecular Biology; Vol. 1184). Humana Press Inc.. https://doi.org/10.1007/978-1-4939-1115-8_15
El-Manzalawy, Yasser ; Honavar, Vasant. / Building classifier ensembles for B-cell epitope prediction. Immunoinformatics. Humana Press Inc., 2014. pp. 285-294 (Methods in Molecular Biology).
@inbook{83a3e8e44a324d4cbd1c06120582b486,
title = "Building classifier ensembles for B-cell epitope prediction",
abstract = "Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or conformational, i.e., amino acids that are often not contiguous in the primary sequence but appear in close proximity within the folded 3D antigen structure. Numerous computational methods have been proposed for predicting both types of B-cell epitopes. However, the development of tools for reliably predicting B-cell epitopes remains a major challenge in immunoinformatics. Classifier ensembles a promising approach for combining a set of classifiers such that the overall performance of the resulting ensemble is better than the predictive performance of the best individual classifier. In this chapter, we show how to build a classifier ensemble for improved prediction of linear B-cell epitopes. The method can be easily adapted to build classifier ensembles for predicting conformational epitopes.",
author = "Yasser El-Manzalawy and Vasant Honavar",
year = "2014",
doi = "10.1007/978-1-4939-1115-8_15",
language = "English (US)",
isbn = "9781493911141",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "285--294",
booktitle = "Immunoinformatics",

}

El-Manzalawy, Y & Honavar, V 2014, Building classifier ensembles for B-cell epitope prediction. in Immunoinformatics. Methods in Molecular Biology, vol. 1184, Humana Press Inc., pp. 285-294. https://doi.org/10.1007/978-1-4939-1115-8_15

Building classifier ensembles for B-cell epitope prediction. / El-Manzalawy, Yasser; Honavar, Vasant.

Immunoinformatics. Humana Press Inc., 2014. p. 285-294 (Methods in Molecular Biology; Vol. 1184).

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Building classifier ensembles for B-cell epitope prediction

AU - El-Manzalawy, Yasser

AU - Honavar, Vasant

PY - 2014

Y1 - 2014

N2 - Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or conformational, i.e., amino acids that are often not contiguous in the primary sequence but appear in close proximity within the folded 3D antigen structure. Numerous computational methods have been proposed for predicting both types of B-cell epitopes. However, the development of tools for reliably predicting B-cell epitopes remains a major challenge in immunoinformatics. Classifier ensembles a promising approach for combining a set of classifiers such that the overall performance of the resulting ensemble is better than the predictive performance of the best individual classifier. In this chapter, we show how to build a classifier ensemble for improved prediction of linear B-cell epitopes. The method can be easily adapted to build classifier ensembles for predicting conformational epitopes.

AB - Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or conformational, i.e., amino acids that are often not contiguous in the primary sequence but appear in close proximity within the folded 3D antigen structure. Numerous computational methods have been proposed for predicting both types of B-cell epitopes. However, the development of tools for reliably predicting B-cell epitopes remains a major challenge in immunoinformatics. Classifier ensembles a promising approach for combining a set of classifiers such that the overall performance of the resulting ensemble is better than the predictive performance of the best individual classifier. In this chapter, we show how to build a classifier ensemble for improved prediction of linear B-cell epitopes. The method can be easily adapted to build classifier ensembles for predicting conformational epitopes.

UR - http://www.scopus.com/inward/record.url?scp=84934441467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84934441467&partnerID=8YFLogxK

U2 - 10.1007/978-1-4939-1115-8_15

DO - 10.1007/978-1-4939-1115-8_15

M3 - Chapter

C2 - 25048130

AN - SCOPUS:84934441467

SN - 9781493911141

T3 - Methods in Molecular Biology

SP - 285

EP - 294

BT - Immunoinformatics

PB - Humana Press Inc.

ER -

El-Manzalawy Y, Honavar V. Building classifier ensembles for B-cell epitope prediction. In Immunoinformatics. Humana Press Inc. 2014. p. 285-294. (Methods in Molecular Biology). https://doi.org/10.1007/978-1-4939-1115-8_15