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Andreatta, Massimo
PRODUCCION CIENTÍFICO TECNOLÓGICA
Mostrando ítems 1-19 de 19
Artículo
Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification
Andreatta, Massimo
;
Karosiene, Edita
;
Rasmussen, Michael
;
Stryhn, Anette
;
Buus, Søren
;
Nielsen, Morten
(
Springer
,
2015-09
)
Artículo
An automated benchmarking platform for MHC class II binding prediction methods
Andreatta, Massimo
;
Trolle, Thomas
;
Yan, Zhen
;
Greenbaum, Jason A
;
Peters, Bjoern
;
Nielsen, Morten
(
Oxford University Press
,
2018-05
)
Artículo
Bioinformatics tools for the prediction of T-cell epitopes
Andreatta, Massimo
;
Nielsen, Morten
(
Springer
,
2018-05
)
Artículo
Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes
Alvarez, Bruno
;
Barra, Carolina M.
;
Nielsen, Morten
;
Andreatta, Massimo
(
Wiley VCH Verlag
,
2018-06
)
Artículo
Footprints of antigen processing boost MHC class II natural ligand predictions
Barra, Carolina M.
;
Alvarez, Bruno
;
Paul, Sinu
;
Sette, Alessandro
;
Peters, Bjoern
;
Andreatta, Massimo
;
Buus, Søren
;
Nielsen, Morten
(
Springer Nature
,
2018-11
)
Artículo
Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
Andreatta, Massimo
;
Nielsen, Morten
(
Oxford University Press
,
2016-02-15
)
Artículo
GibbsCluster: unsupervised clustering and alignment of peptide sequences
Andreatta, Massimo
;
Alvarez, Bruno
;
Nielsen, Morten
(
Oxford University Press
,
2017-04
)
Artículo
IEDB-AR: immune epitope database - analysis resource in 2019
Dhanda, Sandeep Kumar
;
Mahajan, Swapnil
;
Paul, Sinu
;
Yan, Zhen
;
Kim, Haeuk
;
Jespersen, Martin Closter
;
Jurtz, Vanessa
;
Andreatta, Massimo
;
Greenbaum, Jason A
;
Marcatili, Paolo
;
Sette, Alessandro
;
Nielsen, Morten
;
Peters, Bjoern
(
Oxford University Press
,
2019-07-02
)
Artículo
Immunoinformatics: Predicting Peptide–MHC Binding
Nielsen, Morten
;
Andreatta, Massimo
;
Peters, Bjoern
;
Buus, Søren
(
Annual Review
,
2020-07
)
Artículo
Improved methods for predicting peptide binding affinity to MHC class II molecules
Jensen, Kamilla Kjærgaard
;
Andreatta, Massimo
;
Marcatili, Paolo
;
Buus, Søren
;
Greenbaum, Jason A.
;
Yan, Zhen
;
Sette, Alessandro
;
Peters, Bjoern
;
Nielsen, Morten
(
Wiley Blackwell Publishing, Inc
,
2018-07
)
Artículo
Machine learning reveals a non‐canonical mode of peptide binding to MHC class II molecules
Andreatta, Massimo
;
Jurtz, Vanessa I.
;
Kaever, Thomas
;
Sette, Alessandro
;
Peters, Bjoern
;
Nielsen, Morten
(
Wiley Blackwell Publishing, Inc
,
2017-10
)
Artículo
MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments
Andreatta, Massimo
;
Nicastri, Annalisa
;
Peng, Xu
;
Hancock, Gemma
;
Dorrell, Lucy
;
Ternette, Nicola
;
Nielsen, Morten
(
Wiley VCH Verlag
,
2019-02
)
Artículo
NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
DeVette, Christa I.
;
Andreatta, Massimo
;
Bardet, Wilfried
;
Cate, Steven J.
;
Jurtz, Vanessa I.
;
Jackson, Kenneth W.
;
Welm, Alana L.
;
Nielsen, Morten
;
Hildebrand, William H.
(
American Association for Cancer Research
,
2018-06
)
Artículo
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length data sets
Nielsen, Morten
;
Andreatta, Massimo
(
BioMed Central
,
2016-03
)
Artículo
Netmhcpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data
Jurtz, Vanessa
;
Paul, Sinu
;
Andreatta, Massimo
;
Marcatili, Paolo
;
Peters, Bjoern
;
Nielsen, Morten
(
American Association of Immunologists
,
2017-11
)
Artículo
NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions
Alvarez, Bruno
;
Reynisson, Birkir
;
Barra, Carolina M
;
Buus, Søren
;
Ternette, Nicola
;
Connelley, Tim
;
Andreatta, Massimo
;
Nielsen, Morten
(
American Society for Biochemistry and Molecular Biology
,
2019-10
)
Artículo
NNAlign: a platform to construct and evaluate artificial neural network models of receptor–ligand interactions
Nielsen, Morten
;
Andreatta, Massimo
(
Oxford University Press
,
2017-04
)
Artículo
Predicting HLA CD4 immunogenicity in human populations
Dhanda, Sandeep Kumar
;
Karosiene, Edita
;
Edwards, Lindy
;
Grifoni, Alba
;
Paul, Sinu
;
Andreatta, Massimo
;
Weiskopf, Daniela
;
Sidney, John
;
Nielsen, Morten
;
Peters, Bjoern
;
Sette, Alessandro
(
Frontiers Media S.A.
,
2018-06
)
Artículo
Unconventional peptide presentation by major histocompatibility complex (MHC) class i allele HLA-A∗02:01: Breaking confinement
Remesh, Soumya G.
;
Andreatta, Massimo
;
Ying, Ge
;
Kaever, Thomas
;
Nielsen, Morten
;
McMurtrey, Curtis
;
Hildebrand, William
;
Peters, Bjoern
;
Zajonc, Dirk M.
(
American Society for Biochemistry and Molecular Biology
,
2017-03
)
Mostrando ítems 1-19 de 19