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A SUPPORT VECTOR MACHINE APPROACH FOR PREDICTION OF T CELL EPITOPES

LEI HUANG, YANG DAI
2005 Proceedings of the 3rd Asia-Pacific Bioinformatics Conference  
A new peptide encoding scheme is proposed to use with support vector machines for the direct recognition of T cell epitopes.  ...  A procedure of feature selection is also introduced. The computational results demonstrate superior performance over previous techniques.  ...  They would also like to express their gratitude to anonymous referees for useful comments.  ... 
doi:10.1142/9781860947322_0032 fatcat:mwabfzj3i5brpkcu6xl2vlrlim

Computational Methods in Linear B-cell Epitope Prediction

Kavitha KV, Saritha R, Vinod Chandra S S
2013 International Journal of Computer Applications  
This paper reviews various approaches like amino acid scale based methods and machine learning methods used for the prediction of linear B-cell epitopes.  ...  Since experimental methods of identifying epitopes are costly and time consuming, computational methods for prediction are desirable.  ...  BayesB [29] is a Support Vector Machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths .The length varies from 12 to 20.  ... 
doi:10.5120/10520-5498 fatcat:v3ey3xyayzdtjlskzxmfsttpgu

Computational Prediction of B Cell Epitopes from Antigen Sequences [chapter]

Jianzhao Gao, Lukasz Kurgan
2014 Msphere  
We review a comprehensive set of thirteen recent approaches that predict linear and four methods that predict conformational B-cell epitopes from the antigen sequences.  ...  Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released.  ...  JG was supported by the Fundamental Research Funds for the Central Universities grant 65011491.  ... 
doi:10.1007/978-1-4939-1115-8_11 pmid:25048126 fatcat:lxep5wuwanfgrktm7wjban7aeq

On Predicting Conformational B-cell Epitopes: a Comparative Study and a New Model

Khaled A Hassan, Amr Badr, Abdel-Fatah Hegazy
2012 American Journal of Bioinformatics Research  
As a result of this study, we developed a novel computational method "CBCPRED" to predict conformational B-cell epitope residues from the target antigen structure by combining support vector machine model  ...  In this paper, we have carried out a comparative study and discussions for different methods based on the two major computational approaches for predicting conformational B-cell epitopes: sequence-based  ...  ACKNOWLEDGEMENTS It is a pleasure to thank Dr: Yasser EL-Manzalawy for his great support and help.  ... 
doi:10.5923/j.bioinformatics.20110101.02 fatcat:kfsgqavovrggnipig7a6vcgltq

Prediction of Antigenic Epitope Patches on Protein Surface Using Antigen Structure Information and Support Vector Machine

Khaled A Hassan, Amr Badr, Mostafa Abdel-Azim
2012 American Journal of Bioinformatics Research  
The proposed method is a support vector machine based model to predict the epitope patches in the antigen structures by combining the accessible surface area and B-factor structural features.  ...  It p rovides a new approach for the scientists to only use the predicted antigenic epitope surface patch from the target antigen structure in vaccine development rather than using the predicted epitope  ...  ACKNOWLEDGEMENTS It is a pleasure to thank Dr: Yasser EL-Manzalawy for his great support and help.  ... 
doi:10.5923/j.bioinformatics.20120204.03 fatcat:kslgkuawc5aidkeo5gu4aqjyrq

Application of Support Vector Machines in Viral Biology [chapter]

Sonal Modak, Swati Mehta, Deepak Sehgal, Jayaraman Valadi
2019 Global Virology III: Virology in the 21st Century  
Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems.  ...  Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions.  ...  Application of support vector machines for T-cell epitopes prediction [64] Purpose: T-cell epitope prediction with an MHC I restricted T-cell clone.  ... 
doi:10.1007/978-3-030-29022-1_12 fatcat:leaxfnxiuze2jbuyenwps7qcve

PREDICTING FLEXIBLE LENGTH LINEAR B-CELL EPITOPES

Yasser EL-Manzalawy, Drena Dobbs, Vasant Honavar
2008 Computational Systems Bioinformatics  
Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes.  ...  The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector.  ...  Acknowledgments This work was supported in part by a doctoral fellowship from the Egyptian Government to Yasser EL-Manzalawy and a grant from the National Institutes of Health (GM066387) to Vasant Honavar  ... 
doi:10.1142/9781848162648_0011 fatcat:fr7xdueuybh5vjozubkb2r3ewa

Predicting Immunogenicity Risk in Biopharmaceuticals

Nikolet Doneva, Irini Doytchinova, Ivan Dimitrov
2021 Symmetry  
for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations.  ...  The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals.  ...  for predicting the subcellular localization of Gram-negative bacterial proteins based on support vector machine modules [30] .  ... 
doi:10.3390/sym13030388 fatcat:yfgas5oagrgflik46woqv6as2u

Predicting flexible length linear B-cell epitopes

Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar
2008 Computational systems bioinformatics. Computational Systems Bioinformatics Conference  
Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes.  ...  The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector.  ...  Acknowledgments This work was supported in part by a doctoral fellowship from the Egyptian Government to Yasser EL-Manzalawy and a grant from the National Institutes of Health (GM066387) to Vasant Honavar  ... 
pmid:19642274 pmcid:PMC3400678 fatcat:nb6jdbej5zbvlpy2dvtwzmxaxi

Prediction of CTL epitopes using QM, SVM and ANN techniques

Manoj Bhasin, G.P.S. Raghava
2004 Vaccine  
In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes. The method is available at  ...  Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes.  ...  Manoj Bhasin is a recipient of a fellowship from CSIR. This report has IMTECH communication No. 016/2003.  ... 
doi:10.1016/j.vaccine.2004.02.005 pmid:15297074 fatcat:icsmstvsm5hcxcqhoe2yzli44u

Epitope Predictions Indicate the Presence of Two Distinct Types of Epitope-Antibody-Reactivities Determined by Epitope Profiling of Intravenous Immunoglobulins

Mitja Luštrek, Peter Lorenz, Michael Kreutzer, Zilliang Qian, Felix Steinbeck, Di Wu, Nadine Born, Bjoern Ziems, Michael Hecker, Miri Blank, Yehuda Shoenfeld, Zhiwei Cao (+5 others)
2013 PLoS ONE  
A webserver for predicting EAR of peptide sequences is available at www. sysmed-immun.eu/EAR.  ...  Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis.  ...  Elke Schade for technical assistance. Author Contributions  ... 
doi:10.1371/journal.pone.0078605 pmid:24244326 pmcid:PMC3823795 fatcat:d7uj5452zvehbjjb7avpc6g56y

Benchmarking Datasets from Malaria Cytotoxic T-cell Epitopes Using Machine Learning Approach

Rama Adiga
2021 Avicenna journal of medical biotechnology  
Conclusion: The study is the first in-silico study on benchmarking Plasmodium cytotoxic T cell epitope datasets using machine learning approach.  ...  Machine learning classifiers were trained on epitope data using sequence features and comparison of amino acid physicochemical properties was done to yield a valid prediction model.  ...  Anirban Chakraborty, Director of Nitte University Centre for Science Education and Research (NUCSER), Prof. Dr.  ... 
doi:10.18502/ajmb.v13i2.5527 pmid:34012524 pmcid:PMC8112139 fatcat:iwnyy5pvczcibaewadvedrff7e

Application of support vector machines for T-cell epitopes prediction

Y. Zhao, C. Pinilla, D. Valmori, R. Martin, R. Simon
2003 Bioinformatics  
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.  ...  Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development.  ...  Approaches to identify T-cell epitopes based on the prediction of which peptides would be good binders for specific MHC molecules are not accurate, since a functional T-cell response requires adequate  ... 
doi:10.1093/bioinformatics/btg255 pmid:14555632 fatcat:otbruqiyqzcs7hzc2bvhucqowm

Fundamentals and Methods for T- and B-Cell Epitope Prediction

Jose L. Sanchez-Trincado, Marta Gomez-Perosanz, Pedro A. Reche
2017 Journal of Immunology Research  
Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction.  ...  Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.  ...  Acknowledgments The authors wish to thank Inmunotek, SL and the Spanish Department of Science at MINECO for supporting the Immunomedicine group research through Grants SAF2006: 07879, SAF2009:08301, and  ... 
doi:10.1155/2017/2680160 pmid:29445754 pmcid:PMC5763123 fatcat:ybrdo4uyknerdbwictznmvris4

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Syed Nisar Hussain Bukhari, Amit Jain, Ehtishamul Haq, Moaiad Ahmad Khder, Rahul Neware, Jyoti Bhola, Moslem Lari Najafi, Chinmay Chakraborty
2021 Journal of Healthcare Engineering  
The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches  ...  We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids.  ...  Machine Learning Classifiers Used in the Current Study. Classifiers used for the prediction of ZIKV T-cell epitopes are listed in Table 4 .  ... 
doi:10.1155/2021/9591670 pmid:34631001 pmcid:PMC8500748 fatcat:utulldardzd27iyxmrotb5b3le
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