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dc.contributor.author
Mattsson, Andreas Holm
dc.contributor.author
Kringelum, J.V.
dc.contributor.author
Garde, C.
dc.contributor.author
Nielsen, Morten
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dc.date.available
2018-06-15T20:55:57Z
dc.date.issued
2016-12
dc.identifier.citation
Mattsson, Andreas Holm; Kringelum, J.V.; Garde, C.; Nielsen, Morten; Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy; Wiley Blackwell Publishing, Inc; HLA; 88; 6; 12-2016; 287-292
dc.identifier.issn
2059-2310
dc.identifier.uri
http://hdl.handle.net/11336/48877
dc.description.abstract
Pan-specific prediction of receptor–ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor–ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization. The success of this approach has however proven to depend strongly on the similarity of the query molecule to the molecules with characterized specificity using in the machine-learning process. Here, we outline an alternative strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor–ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand and MHC epitope identification perform better than the conventional ligand clustering method on the alleles with remote similarity to the training set.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley Blackwell Publishing, Inc
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Artificial Neural Networks
dc.subject
Clustering
dc.subject
Mhc Binding Specificity
dc.subject
Mhc Class I
dc.subject
Peptide–Mhc Binding
dc.subject
T-Cell Epitope
dc.subject.classification
Salud Ocupacional
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dc.subject.classification
Ciencias de la Salud
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-06-13T14:57:36Z
dc.journal.volume
88
dc.journal.number
6
dc.journal.pagination
287-292
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Oxford
dc.description.fil
Fil: Mattsson, Andreas Holm. Technical University of Denmark; Dinamarca. Evaxion Biotech; Dinamarca
dc.description.fil
Fil: Kringelum, J.V.. Evaxion Biotech; Dinamarca
dc.description.fil
Fil: Garde, C.. Universidad de Copenhagen; Dinamarca
dc.description.fil
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
dc.journal.title
HLA
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/tan.12911
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/tan.12911
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