Vanessa Jurtz
Vanessa Jurtz
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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
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
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
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
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
HostPhinder: a phage host prediction tool
J Villarroel, KA Kleinheinz, VI Jurtz, H Zschach, O Lund, M Nielsen, ...
Viruses 8 (5), 116, 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, 1566, 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
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
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
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
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), 1-12, 2019
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), 1-13, 2021
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
NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine TumorsNetH2pan: A Multi-Allele Predictor for Murine MHCI Epitopes
CI DeVette, M Andreatta, W Bardet, SJ Cate, VI Jurtz, KW Jackson, ...
Cancer immunology research 6 (6), 636-644, 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
The CGE tool box
MV Larsen, KG Joensen, E Zankari, J Ahrenfeldt, O Lukjancenko, ...
Applied genomics of foodborne pathogens, 65-90, 2017
Computational methods for identification of T cell neoepitopes in tumors
VI Jurtz, LR Olsen
Cancer Bioinformatics, 157-172, 2019
Deep learning reveals 3D atherosclerotic plaque distribution and composition
VI Jurtz, G Skovbjerg, CG Salinas, U Roostalu, L Pedersen, ...
Scientific reports 10 (1), 1-9, 2020
NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. bioRxiv p 433706
VI Jurtz
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