Literature DB >> 14555632

Application of support vector machines for T-cell epitopes prediction.

Yingdong Zhao1, Clemencia Pinilla, Danila Valmori, Roland Martin, Richard Simon.   

Abstract

MOTIVATION: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development.
RESULTS: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding. SUPPLEMENTARY INFORMATION: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf

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Year:  2003        PMID: 14555632     DOI: 10.1093/bioinformatics/btg255

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

1.  Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles.

Authors:  Pedro A Reche; John-Paul Glutting; Hong Zhang; Ellis L Reinherz
Journal:  Immunogenetics       Date:  2004-09-03       Impact factor: 2.846

Review 2.  Major histocompatibility complex class I binding predictions as a tool in epitope discovery.

Authors:  Claus Lundegaard; Ole Lund; Søren Buus; Morten Nielsen
Journal:  Immunology       Date:  2010-05-26       Impact factor: 7.397

3.  SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

Authors:  Siwen Hu-Lieskovan; Srabani Bhaumik; Kavita Dhodapkar; Jean-Charles J B Grivel; Sumati Gupta; Brent A Hanks; Sylvia Janetzki; Thomas O Kleen; Yoshinobu Koguchi; Amanda W Lund; Cristina Maccalli; Yolanda D Mahnke; Ruslan D Novosiadly; Senthamil R Selvan; Tasha Sims; Yingdong Zhao; Holden T Maecker
Journal:  J Immunother Cancer       Date:  2020-12       Impact factor: 13.751

4.  Design of enhanced agonists through the use of a new virtual screening method: application to peptides that bind class I major histocompatibility complex (MHC) molecules.

Authors:  Sergio Madurga; Ignasi Belda; Xavier Llorà; Ernest Giralt
Journal:  Protein Sci       Date:  2005-08       Impact factor: 6.725

5.  MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties.

Authors:  Juan Cui; Lian Yi Han; Hong Huang Lin; Zhi Qun Tang; Li Jiang; Zhi Wei Cao; Yu Zong Chen
Journal:  Immunogenetics       Date:  2006-07-11       Impact factor: 2.846

6.  Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors.

Authors:  Ovidiu Ivanciuc; Werner Braun
Journal:  Protein Pept Lett       Date:  2007       Impact factor: 1.890

Review 7.  Advances in the study of HLA-restricted epitope vaccines.

Authors:  Lingxiao Zhao; Min Zhang; Hua Cong
Journal:  Hum Vaccin Immunother       Date:  2013-08-16       Impact factor: 3.452

Review 8.  A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

Authors:  Hamdan O Alanazi; Abdul Hanan Abdullah; Kashif Naseer Qureshi
Journal:  J Med Syst       Date:  2017-03-11       Impact factor: 4.460

9.  Prediction of supertype-specific HLA class I binding peptides using support vector machines.

Authors:  Guang Lan Zhang; Ivana Bozic; Chee Keong Kwoh; J Thomas August; Vladimir Brusic
Journal:  J Immunol Methods       Date:  2007-01-25       Impact factor: 2.303

10.  Epitope discovery with phylogenetic hidden Markov models.

Authors:  Miguel Lacerda; Konrad Scheffler; Cathal Seoighe
Journal:  Mol Biol Evol       Date:  2010-01-20       Impact factor: 16.240

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