Morten Nielsen
Morten Nielsen
Department of Health Technology, The Technical University of
Verified email at
Cited by
Cited by
Improved method for predicting linear B-cell epitopes
JEP Larsen, O Lund, M Nielsen
Immunome research 2 (1), 1-7, 2006
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
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
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), 51, 2009
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), 1, 2009
Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures
P Haste Andersen, M Nielsen, O Lund
Protein Science 15 (11), 2558-2567, 2006
Gapped sequence alignment using artificial neural networks: application to the MHC class I system
M Andreatta, M Nielsen
Bioinformatics 32 (4), 511-517, 2016
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
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
NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
M Nielsen, O Lund
BMC bioinformatics 10 (1), 296, 2009
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), 424, 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), 238, 2007
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), 568, 2010
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 (1-2), 33-41, 2005
CPHmodels-3.0—remote homology modeling using structure-guided sequence profiles
M Nielsen, C Lundegaard, O Lund, TN Petersen
Nucleic acids research 38 (suppl_2), W576-W581, 2010
Reliable B cell epitope predictions: impacts of method development and improved benchmarking
JV Kringelum, C Lundegaard, O Lund, M Nielsen
PLoS Comput Biol 8 (12), e1002829, 2012
Immune epitope database analysis resource
Y Kim, J Ponomarenko, Z Zhu, D Tamang, P Wang, J Greenbaum, ...
Nucleic acids research 40 (W1), W525-W530, 2012
A community resource benchmarking predictions of peptide binding to MHC-I molecules
B Peters, HH Bui, S Frankild, M Nielsen, C Lundegaard, E Kostem, ...
PLoS Comput Biol 2 (6), e65, 2006
Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach
M Nielsen, C Lundegaard, P Worning, CS Hvid, K Lamberth, S Buus, ...
Bioinformatics 20 (9), 1388-1397, 2004
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
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