| 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 | 1409 | 2017 |
| 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 | 598 | 2019 |
| IEDB-AR: immune epitope database—analysis resource in 2019 SK Dhanda, S Mahajan, S Paul, Z Yan, H Kim, MC Jespersen, V Jurtz, ... Nucleic acids research 47 (W1), W502-W506, 2019 | 351 | 2019 |
| A bacterial analysis platform: an integrated system for analysing bacterial whole genome sequencing data for clinical diagnostics and surveillance MCF Thomsen, J Ahrenfeldt, JLB Cisneros, V Jurtz, MV Larsen, ... PloS one 11 (6), e0157718, 2016 | 184 | 2016 |
| An introduction to deep learning on biological sequence data: examples and solutions VI Jurtz, AR Johansen, M Nielsen, JJ Almagro Armenteros, H Nielsen, ... Bioinformatics 33 (22), 3685-3690, 2017 | 180 | 2017 |
| NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data A Montemurro, V Schuster, HR Povlsen, AK Bentzen, V Jurtz, ... Communications biology 4 (1), 1060, 2021 | 176 | 2021 |
| HostPhinder: a phage host prediction tool J Villarroel, KA Kleinheinz, VI Jurtz, H Zschach, O Lund, M Nielsen, ... Viruses 8 (5), 116, 2016 | 160 | 2016 |
| An analysis of natural T cell responses to predicted tumor neoepitopes AM Bjerregaard, M Nielsen, V Jurtz, CM Barra, SR Hadrup, Z Szallasi, ... Frontiers in immunology 8, 1566, 2017 | 118 | 2017 |
| NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks VI Jurtz, LE Jessen, AK Bentzen, MC Jespersen, S Mahajan, R Vita, ... BioRxiv, 433706, 2018 | 106 | 2018 |
| MetaPhinder—identifying bacteriophage sequences in metagenomic data sets VI Jurtz, J Villarroel, O Lund, M Voldby Larsen, M Nielsen PLoS One 11 (9), e0163111, 2016 | 76 | 2016 |
| Determination of a predictive cleavage motif for eluted major histocompatibility complex class II ligands S Paul, E Karosiene, SK Dhanda, V Jurtz, L Edwards, M Nielsen, A Sette, ... Frontiers in immunology 9, 1795, 2018 | 60 | 2018 |
| TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes KK Jensen, V Rantos, EC Jappe, TH Olsen, MC Jespersen, V Jurtz, ... Scientific reports 9 (1), 14530, 2019 | 56 | 2019 |
| Quantitative whole-brain 3D imaging of tyrosine hydroxylase-labeled neuron architecture in the mouse MPTP model of Parkinson's disease U Roostalu, CBG Salinas, DD Thorbek, JL Skytte, K Fabricius, P Barkholt, ... Disease models & mechanisms 12 (11), dmm042200, 2019 | 49 | 2019 |
| Machine learning reveals a non‐canonical mode of peptide binding to MHC class II molecules M Andreatta, VI Jurtz, T Kaever, A Sette, B Peters, M Nielsen Immunology 152 (2), 255-264, 2017 | 31 | 2017 |
| NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors CI DeVette, M Andreatta, W Bardet, SJ Cate, VI Jurtz, KW Jackson, ... Cancer immunology research 6 (6), 636-644, 2018 | 23 | 2018 |
| A generic deep convolutional neural network framework for prediction of receptor–ligand interactions—NetPhosPan: application to kinase phosphorylation prediction E Fenoy, JMG Izarzugaza, V Jurtz, S Brunak, M Nielsen Bioinformatics 35 (7), 1098-1107, 2019 | 16 | 2019 |
| NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. bioRxiv 433706 VI Jurtz, LE Jessen, AK Bentzen, MC Jespersen, S Mahajan, R Vita, ... Preprint] 3, 2018 | 13 | 2018 |
| Computational methods for identification of T cell neoepitopes in tumors VI Jurtz, LR Olsen Cancer Bioinformatics, 157-172, 2019 | 10 | 2019 |
| The CGE tool box MV Larsen, KG Joensen, E Zankari, J Ahrenfeldt, O Lukjancenko, ... Applied genomics of foodborne pathogens, 65-90, 2017 | 10 | 2017 |
| Deep learning reveals 3D atherosclerotic plaque distribution and composition VI Jurtz, G Skovbjerg, CG Salinas, U Roostalu, L Pedersen, ... Scientific Reports 10 (1), 21523, 2020 | 9 | 2020 |
Morten NielsenDepartment of Health Technology, The Technical University ofVerified email at cbs.dtu.dk