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Novel Machine Learning Methods for MHC Class I Binding Prediction [chapter]

Christian Widmer, Nora C. Toussaint, Yasemin Altun, Oliver Kohlbacher, Gunnar Rätsch
2010 Lecture Notes in Computer Science  
We propose two approaches to improve the predictive power of kernelbased Machine Learning methods for MHC class I binding prediction: First, a modification of the Weighted Degree string kernel that allows  ...  Prediction of such MHC class I binding peptides is a vital step in the design of peptide-based vaccines and therefore one of the major problems in computational immunology.  ...  Conclusion We have proposed two approaches to improve kernel-based Machine Learning methods for MHC class I binding prediction.  ... 
doi:10.1007/978-3-642-16001-1_9 fatcat:zy3lqfti75cp5kqwp2ilxsrx3i

Prediction of MHC class I binding peptides, using SVMHC

Pierre Dönnes, Arne Elofsson
2002 BMC Bioinformatics  
Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules.  ...  Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types.  ...  Acknowledgements This work was supported by grants form the Swedish Natural Sciences Research Council and the Swedish Research Council for Engineering Sciences to AE.  ... 
pmid:12225620 pmcid:PMC129981 fatcat:dphljzns6fgtvjosfilwor2imu

:{unav)

Pierre Dönnes, Arne Elofsson
2016 BMC Bioinformatics  
Results: Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules.  ...  Conclusions: Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types.  ...  Acknowledgements This work was supported by grants form the Swedish Natural Sciences Research Council and the Swedish Research Council for Engineering Sciences to AE.  ... 
doi:10.1186/1471-2105-3-25 fatcat:ojyp2pxpwjal5irvgkfmviqf34

Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide Distance Functions [chapter]

Chen Yanover, Tomer Hertz
2005 Lecture Notes in Computer Science  
We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors.  ...  Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose a novel approach for predicting binding affinity.  ...  We thank Daphna Weinshall for many fruitful discussions and comments. C.Y is supported by Yeshaya Horowitz Association through the Center for Complexity Science.  ... 
doi:10.1007/11415770_34 fatcat:ev7upuhj5rcrjd3ckxw4ypqipq

Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature

Tianyi Zhao, Liang Cheng, Tianyi Zang, Yang Hu
2019 Frontiers in Genetics  
Nowadays more and more machine learning methods have been developed to predict binding affinity and some of them have become the popular tools.  ...  Peptide-based vaccine development needs accurate prediction of the binding affinity between major histocompatibility complex I (MHC I) proteins and their peptide ligands.  ...  CONCLUsIONs In this paper, we purposed a novel method for peptide-MHC-I binding prediction.  ... 
doi:10.3389/fgene.2019.01191 pmid:31850062 pmcid:PMC6892951 fatcat:2nlgr7d4xrgeno3rk6c6dsdmzq

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.  ...  In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design.  ...  The model prediction for the MHC I model is less challenging than those for MHC class II; the reason is that for MHC I, the difference in the peptide length is negligible, while for MHC class II the length  ... 
doi:10.3390/sym13030388 fatcat:yfgas5oagrgflik46woqv6as2u

PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

Tomer Hertz, Chen Yanover
2006 BMC Bioinformatics  
Results: We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors.  ...  We have recently uploaded the PepDist webserver which provides binding prediction of peptides to 35 different MHC class I alleles.  ...  Acknowledgements We thank Daphna Weinshall for many fruitful discussions and comments. We also thank Itamar Glatzer for his help in the implementation of the Pep-Dist webserver.  ... 
doi:10.1186/1471-2105-7-s1-s3 pmid:16723006 pmcid:PMC1810314 fatcat:ropuby2crvby5dtte7fz2srnge

Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers
English

Gomase VS, Yash Parekh, Subin Koshy, Siddhesh Lakhan
2009 International Journal of Genetics  
The machine learning techniques are playing a major role in the field of immunoinformatics for DNA-binding domain analysis.  ...  This study shows active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens.  ...  Support Vector Machine (SVM) based method for prediction of promiscuous MHC class II binding peptides.  ... 
doi:10.9735/0975-2862.1.1.1-5 fatcat:uy2owgq2dzhztit27ppiymioay

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

Birkir Reynisson, Bruno Alvarez, Sinu Paul, Bjoern Peters, Morten Nielsen
2020 Nucleic Acids Research  
MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II).  ...  Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors.  ...  MHC comes in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II).  ... 
doi:10.1093/nar/gkaa379 pmid:32406916 pmcid:PMC7319546 fatcat:rwwup4thw5eh3fbe67c7ucijuq

Immunoinformatics: Predicting Peptide–MHC Binding

Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus
2020 Annual Review of Biomedical Data Science  
Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules  ...  Computational tools for the prediction of peptide–MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research.  ...  ACKNOWLEDGMENTS This work was supported through funding from NIH (National Institutes of Health) contract 75N93019C00001 for the Immune Epitope Database and the Danish MRC (Medical Research Council) award  ... 
doi:10.1146/annurev-biodatasci-021920-100259 fatcat:qam42i2a2nhptbqganzpbcqree

T Cell Epitope Predictions

Bjoern Peters, Morten Nielsen, Alessandro Sette
2020 Annual Review of Immunology  
Expected final online publication date for the Annual Review of Immunology, Volume 38 is April 26, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.  ...  Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes.  ...  THE ADVENT OF MACHINE LEARNING TO PREDICT MHC BINDING Driven by the success of the heuristic predictions, more advanced supervised machine learning approaches were soon proposed.  ... 
doi:10.1146/annurev-immunol-082119-124838 pmid:32045313 fatcat:x5z4f7equvbkldtzj6m7inxxsy

Predicting MHC-II Binding Affinity Using Multiple Instance Regression

Yasser EL-Manzalawy, Drena Dobbs, Vasant Honavar
2011 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression.  ...  An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at  ...  (iii) MHCMIR, a novel method for predicting the binding affinity of flexible length MHC-II peptides using MILESreg.  ... 
doi:10.1109/tcbb.2010.94 pmid:20855923 pmcid:PMC3400677 fatcat:ssjrr72s25a5bb233r53euizfi

Peptide Binding at Class I Major Histocompatibility Complex Scored with Linear Functions and Support Vector Machines

Henning Riedesel, Björn Kolbeck, Oliver Schmetzer, Ernst-Walter Knapp
2004 Genome Informatics Series  
We explore two different methods to predict the binding ability of nonapeptides at the class I major histocompatibility complex using a general linear scoring function that defines a separating hyperplane  ...  With the generalized LSM impaired data for learning with a small set of binding peptides and a large set of non-binding peptides can be treated in a balanced way rendering LSM more successful than SVM,  ...  Acknowledgments We are grateful for financial support from the Deutsche Forschungsgemeinschaft: Sfb498, GRK80/2, GRK268, GRK788/1, Forschergruppe 475.  ... 
doi:10.11234/gi1990.15.198 fatcat:2ufajhnm65hidnbhaom7zhfru4

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction

Youngmahn Han, Dongsup Kim
2017 BMC Bioinformatics  
We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN.  ...  Conclusions: We developed a novel method for peptide-HLA-I binding predictions using DCNN trained on ILA data that encode peptide binding data and demonstrated the reliable performance of the DCNN in nonapeptide  ...  Hong for helpful discussions and comments. Funding Availability of data and materials ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/ convmhc.  ... 
doi:10.1186/s12859-017-1997-x pmid:29281985 pmcid:PMC5745637 fatcat:otkypr6io5dn7bvcqf2hm6htfq

Peptide-Based Vaccinology: Experimental and Computational Approaches to Target Hypervariable Viruses through the Fine Characterization of Protective Epitopes Recognized by Monoclonal Antibodies and the Identification of T-Cell-Activating Peptides

Matteo Castelli, Francesca Cappelletti, Roberta Antonia Diotti, Giuseppe Sautto, Elena Criscuolo, Matteo Dal Peraro, Nicola Clementi
2013 Clinical and Developmental Immunology  
Here, we review several strategies based on experimental techniques alone or addressed byin silicoanalysis that are frequently used to predict immunogens to be included in novel epitope-based vaccine approaches  ...  This is particularly important for the selective targeting of conserved regions shared among hypervariable viruses.  ...  MHC I binding, binding to MHC and TCR recognitions.  ... 
doi:10.1155/2013/521231 pmid:23878584 pmcid:PMC3710646 fatcat:njrtlpk7bfaolbtmrl6i4oofue
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