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Morten Nielsen

Showing results (1-10 of 292) with videos related to

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Genome Medicine|April 1, 2016
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasetsMorten Nielsen, Massimo Andreatta
Methods in Molecular Biology (Clifton, N.J.)|October 2, 2015
Prediction of Antibody EpitopesMorten Nielsen, Paolo Marcatili
Immunogenetics|May 28, 2014
NetTepi: an integrated method for the prediction of T cell epitopesThomas Trolle, Morten Nielsen
Bioinformatics (Oxford, England)|October 31, 2015
Gapped sequence alignment using artificial neural networks: application to the MHC class I systemMassimo Andreatta, Morten Nielsen
Immunology|February 23, 2012
Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlignMassimo Andreatta, Morten Nielsen
Nucleic Acids Research|April 14, 2017
NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactionsMorten Nielsen, Massimo Andreatta
Methods in Molecular Biology (Clifton, N.J.)|May 2, 2018
Bioinformatics Tools for the Prediction of T-Cell EpitopesMassimo Andreatta, Morten Nielsen
BMC Bioinformatics|September 22, 2009
NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding predictionMorten Nielsen, Ole Lund
Elife|March 4, 2024
Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integrationMathias Fynbo Jensen, Morten Nielsen
Frontiers in Immunology|August 1, 2025
NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactionsSebastian N Deleuran, Morten Nielsen
Pageof 30

Showing results (1-10 of 292) with videos related to

Sort By:
Pageof 30
Genome Medicine|April 1, 2016
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasetsMorten Nielsen, Massimo Andreatta
Methods in Molecular Biology (Clifton, N.J.)|October 2, 2015
Prediction of Antibody EpitopesMorten Nielsen, Paolo Marcatili
Immunogenetics|May 28, 2014
NetTepi: an integrated method for the prediction of T cell epitopesThomas Trolle, Morten Nielsen
Bioinformatics (Oxford, England)|October 31, 2015
Gapped sequence alignment using artificial neural networks: application to the MHC class I systemMassimo Andreatta, Morten Nielsen
Immunology|February 23, 2012
Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlignMassimo Andreatta, Morten Nielsen
Nucleic Acids Research|April 14, 2017
NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactionsMorten Nielsen, Massimo Andreatta
Methods in Molecular Biology (Clifton, N.J.)|May 2, 2018
Bioinformatics Tools for the Prediction of T-Cell EpitopesMassimo Andreatta, Morten Nielsen
BMC Bioinformatics|September 22, 2009
NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding predictionMorten Nielsen, Ole Lund
Elife|March 4, 2024
Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integrationMathias Fynbo Jensen, Morten Nielsen
Frontiers in Immunology|August 1, 2025
NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactionsSebastian N Deleuran, Morten Nielsen
Pageof 30