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FRED—a framework for T-cell epitope detection
Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different meth...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759545/ https://www.ncbi.nlm.nih.gov/pubmed/19578173 http://dx.doi.org/10.1093/bioinformatics/btp409 |
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author | Feldhahn, Magdalena Dönnes, Pierre Thiel, Philipp Kohlbacher, Oliver |
author_facet | Feldhahn, Magdalena Dönnes, Pierre Thiel, Philipp Kohlbacher, Oliver |
author_sort | Feldhahn, Magdalena |
collection | PubMed |
description | Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods. Availability: FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED. Contact: feldhahn@informatik.uni-tuebingen.de |
format | Text |
id | pubmed-2759545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-27595452009-10-15 FRED—a framework for T-cell epitope detection Feldhahn, Magdalena Dönnes, Pierre Thiel, Philipp Kohlbacher, Oliver Bioinformatics Applications Note Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods. Availability: FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED. Contact: feldhahn@informatik.uni-tuebingen.de Oxford University Press 2009-10-15 2009-07-06 /pmc/articles/PMC2759545/ /pubmed/19578173 http://dx.doi.org/10.1093/bioinformatics/btp409 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Feldhahn, Magdalena Dönnes, Pierre Thiel, Philipp Kohlbacher, Oliver FRED—a framework for T-cell epitope detection |
title | FRED—a framework for T-cell epitope detection |
title_full | FRED—a framework for T-cell epitope detection |
title_fullStr | FRED—a framework for T-cell epitope detection |
title_full_unstemmed | FRED—a framework for T-cell epitope detection |
title_short | FRED—a framework for T-cell epitope detection |
title_sort | fred—a framework for t-cell epitope detection |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759545/ https://www.ncbi.nlm.nih.gov/pubmed/19578173 http://dx.doi.org/10.1093/bioinformatics/btp409 |
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