Massimo Andreatta
Massimo Andreatta
Ludwig Institute for Cancer Research, Lausanne
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Cited by
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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-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-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), 1-9, 2016
Using electronic patient records to discover disease correlations and stratify patient cohorts
FS Roque, PB Jensen, H Schmock, M Dalgaard, M Andreatta, T Hansen, ...
PLoS Comput Biol 7 (8), e1002141, 2011
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
Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification
M Andreatta, E Karosiene, M Rasmussen, A Stryhn, S Buus, M Nielsen
Immunogenetics 67 (11), 641-650, 2015
Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach
M Andreatta, O Lund, M Nielsen
Bioinformatics 29 (1), 8-14, 2013
GibbsCluster: unsupervised clustering and alignment of peptide sequences
M Andreatta, B Alvarez, M Nielsen
Nucleic acids research 45 (W1), W458-W463, 2017
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
NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data
M Andreatta, C Schafer-Nielsen, O Lund, S Buus, M Nielsen
PloS one 6 (11), e26781, 2011
An automated benchmarking platform for MHC class II binding prediction methods
M Andreatta, T Trolle, Z Yan, JA Greenbaum, B Peters, M Nielsen
Bioinformatics 34 (9), 1522-1528, 2018
Unconventional peptide presentation by major histocompatibility complex (MHC) class I allele HLA-A* 02: 01: BREAKING CONFINEMENT
SG Remesh, M Andreatta, G Ying, T Kaever, M Nielsen, C McMurtrey, ...
Journal of Biological Chemistry 292 (13), 5262-5270, 2017
Predicting HLA CD4 immunogenicity in human populations
SK Dhanda, E Karosiene, L Edwards, A Grifoni, S Paul, M Andreatta, ...
Frontiers in immunology 9, 1369, 2018
Footprints of antigen processing boost MHC class II natural ligand predictions
C Barra, B Alvarez, S Paul, A Sette, B Peters, M Andreatta, S Buus, ...
Genome medicine 10 (1), 1-15, 2018
NNAlign: a platform to construct and evaluate artificial neural network models of receptor–ligand interactions
M Nielsen, M Andreatta
Nucleic acids research 45 (W1), W344-W349, 2017
Computational tools for the identification and interpretation of sequence motifs in immunopeptidomes
B Alvarez, C Barra, M Nielsen, M Andreatta
Proteomics 18 (12), 1700252, 2018
Characterizing the binding motifs of 11 common human HLA‐DP and HLA‐DQ molecules using NNAlign
M Andreatta, M Nielsen
Immunology 136 (3), 306-311, 2012
NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions
B Alvarez, B Reynisson, C Barra, S Buus, N Ternette, T Connelley, ...
Molecular & Cellular Proteomics 18 (12), 2459-2477, 2019
In silico prediction of human pathogenicity in the γ-proteobacteria
M Andreatta, M Nielsen, FM Aarestrup, O Lund
PloS one 5 (10), e13680, 2010
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
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