| 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 | 1995 | 2020 |
| Improved method for predicting linear B-cell epitopes JEP Larsen, O Lund, M Nielsen Immunome research 2, 1-7, 2006 | 1574 | 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 | 1469 | 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 | 1391 | 2020 |
| 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 | 1360 | 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 | 1327 | 2003 |
| Gapped sequence alignment using artificial neural networks: application to the MHC class I system M Andreatta, M Nielsen Bioinformatics 32 (4), 511-517, 2016 | 1158 | 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 | 927 | 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 | 927 | 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 | 850 | 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 | 786 | 2018 |
| 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 | 745 | 2006 |
| 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 | 743 | 2009 |
| 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 | 719 | 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 | 717 | 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 | 687 | 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 | 666 | 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 | 663 | 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 | 605 | 2005 |
| NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning MS Klausen, MC Jespersen, H Nielsen, KK Jensen, VI Jurtz, ... Proteins: Structure, Function, and Bioinformatics 87 (6), 520-527, 2019 | 582 | 2019 |
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