Morten Nielsen
Morten Nielsen
Department of Health Technology, The Technical University of
Verified email at
Cited by
Cited by
Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19
T Sekine, A Perez-Potti, O Rivera-Ballesteros, K Strålin, JB Gorin, ...
Cell 183 (1), 158-168. e14, 2020
Improved method for predicting linear B-cell epitopes
JEP Larsen, O Lund, M Nielsen
Immunome research 2, 1-7, 2006
BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
MC Jespersen, B Peters, M Nielsen, P Marcatili
Nucleic acids research 45 (W1), W24-W29, 2017
Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
M Nielsen, C Lundegaard, P Worning, SL Lauemøller, K Lamberth, ...
Protein Science 12 (5), 1007-1017, 2003
NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data
V Jurtz, S Paul, M Andreatta, P Marcatili, B Peters, M Nielsen
The Journal of Immunology 199 (9), 3360-3368, 2017
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
B Reynisson, B Alvarez, S Paul, B Peters, M Nielsen
Nucleic acids research 48 (W1), W449-W454, 2020
Gapped sequence alignment using artificial neural networks: application to the MHC class I system
M Andreatta, M Nielsen
Bioinformatics 32 (4), 511-517, 2016
NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11
C Lundegaard, K Lamberth, M Harndahl, S Buus, O Lund, M Nielsen
Nucleic acids research 36 (suppl_2), W509-W512, 2008
Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction
MV Larsen, C Lundegaard, K Lamberth, S Buus, O Lund, M Nielsen
BMC bioinformatics 8, 1-12, 2007
NetMHCpan, a method for MHC class I binding prediction beyond humans
I Hoof, B Peters, J Sidney, LE Pedersen, A Sette, O Lund, S Buus, ...
Immunogenetics 61, 1-13, 2009
Improved methods for predicting peptide binding affinity to MHC class II molecules
KK Jensen, M Andreatta, P Marcatili, S Buus, JA Greenbaum, Z Yan, ...
Immunology 154 (3), 394-406, 2018
A generic method for assignment of reliability scores applied to solvent accessibility predictions
B Petersen, TN Petersen, P Andersen, M Nielsen, C Lundegaard
BMC structural biology 9, 1-10, 2009
Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures
P Haste Andersen, M Nielsen, OLE Lund
Protein Science 15 (11), 2558-2567, 2006
Peptide binding predictions for HLA DR, DP and DQ molecules
P Wang, J Sidney, Y Kim, A Sette, O Lund, M Nielsen, B Peters
BMC bioinformatics 11, 1-12, 2010
Reliable B cell epitope predictions: impacts of method development and improved benchmarking
JV Kringelum, C Lundegaard, O Lund, M Nielsen
PLoS computational biology 8 (12), e1002829, 2012
NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and-B locus protein of known sequence
M Nielsen, C Lundegaard, T Blicher, K Lamberth, M Harndahl, S Justesen, ...
PloS one 2 (8), e796, 2007
Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method
M Nielsen, C Lundegaard, O Lund
BMC bioinformatics 8, 1-12, 2007
NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
M Nielsen, O Lund
BMC bioinformatics 10, 1-10, 2009
The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage
M Nielsen, C Lundegaard, O Lund, C Keşmir
Immunogenetics 57, 33-41, 2005
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets
M Nielsen, M Andreatta
Genome medicine 8, 1-9, 2016
The system can't perform the operation now. Try again later.
Articles 1–20