M. Nielsen

O. Lund, M. Nielsen, S. Buus, 2010, Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation.

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

T. Mora, A. Walczak, M. Nielsen, 2022, bioRxiv.

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

L. Cowell, S. Christley, J. Greenbaum, 2020, bioRxiv.

T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities

Paolo Marcatili, Esteban Lanzarotti, Morten Nielsen, 2019, Front. Immunol..

Machine learning competition in immunology - Prediction of HLA class I binding peptides.

Harris Papadopoulos, Tomer Hertz, Vladimir Brusic, 2011, Journal of immunological methods.

Sequencing swine leucocyte alleles for vaccine development

M. Nielsen, S. Buus, R. Bishop, 2018 .

T cell receptor fingerprinting enables in-depth characterization of the interactions governing recognition of peptide–MHC complexes

Morten Nielsen, Paolo Marcatili, Sine R Hadrup, 2018, Nature Biotechnology.

Identification of immediate early gene products of bovine herpes virus 1 (BHV-1) as dominant antigens recognized by CD8 T cells in immune cattle.

Morten Nielsen, M. Nielsen, W. Morrison, 2017, The Journal of general virology.

Immunoinformatics: Predicting Peptide-MHC Binding.

Morten Nielsen, Bjoern Peters, Massimo Andreatta, 2020, Annual review of biomedical data science.

SARS CTL vaccine candidates; HLA supertype‐, genome‐wide scanning and biochemical validation

S. Brunak, M. Nielsen, O. Lund, 2004, Tissue antigens.

The peptide-binding specificity of HLA-A*3001 demonstrates membership of the HLA-A3 supertype

Morten Nielsen, Ole Lund, Claus Lundegaard, 2008, Immunogenetics.

Identification of CD8+ T Cell Epitopes in the West Nile Virus Polyprotein by Reverse-Immunology Using NetCTL

Morten Nielsen, Ole Lund, Søren Buus, 2010, PloS one.

NNAlign_MA; semi-supervised MHC peptidome deconvolution for accurate characterization of MHC binding motifs and improved T cell epitope prediction

Massimo Andreatta, Morten Nielsen, Bruno Alvarez, 2019 .

MHC-I Ligand Discovery Using Targeted Database Searches of Mass Spectrometry Data: Implications for T-Cell Immunotherapies.

Morten Nielsen, Prathyusha Konda, Shashi Gujar, 2017, Journal of proteome research.

Extraordinary peptide-binding mode of a songbird MHC class-I molecule suggests mechanism to counter pathogen immune evasion

M. Nielsen, S. Buus, K. Lindkvist-Petersson, 2023, bioRxiv.

Selective HLA restriction enables the evaluation and interpretation of immunogenic breadth at comparable levels to that observed with broader HLA distribution

M. Nielsen, A. Fiore-Gartland, E. Hunter, 2021, Proteomics.

Selective HLA Restriction Permits the Evaluation and Interpretation of Immunogenic Breadth at Comparable Levels to Autologous HLA

M. Nielsen, R. Rosenthal, A. Fiore-Gartland, 2020 .

A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

M. Nielsen, S. Buus, M. Rasmussen, 2015, Immunogenetics.

Characterization of binding specificities of bovine leucocyte class I molecules: impacts for rational epitope discovery

Morten Nielsen, Søren Buus, Mikkel Harndahl, 2014, Immunogenetics.

The Effect of a Tropical Climate on Available Nutrient Resources to Springs in Ophiolite-Hosted, Deep Biosphere Ecosystems in the Philippines

Morten Nielsen, Bjoern Peters, John Sidney, 2017, Front. Microbiol..

NetTepi: an integrated method for the prediction of T cell epitopes

M. Nielsen, Thomas Trolle, 2014, Immunogenetics.

Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity

M. Nielsen, S. Buus, M. Harndahl, 2012 .

Morten Nielsen, Ole Lund, Sune Frankild, 2008, PloS one.

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

O. Lund, M. Nielsen, S. Buus, 2013, Immunogenetics.

MHC motif viewer

Morten Nielsen, Ole Lund, Nicolas Rapin, 2008, Immunogenetics.

The interplay of sequence conservation and T cell immune recognition

Morten Nielsen, Alessandro Sette, Jason Greenbaum, 2014, BCB.

Sequence diversity between class I MHC loci of African native and introduced Bos taurus cattle in Theileria parva endemic regions: in silico peptide binding prediction identifies distinct functional clusters

M. Nielsen, C. Mazzoni, I. Obara, 2016, Immunogenetics.

Immunogenicity of HLA Class I and II Double Restricted Influenza A-Derived Peptides

S. R. Pedersen, M. Nielsen, S. Buus, 2016, PloS one.

A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01

M. Nielsen, S. Buus, M. Harndahl, 2016, Immunogenetics.

Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities

Morten Nielsen, Ole Lund, Kasper Lamberth, 2011, Immunogenetics.

The validity of predicted T-cell epitopes.

Morten Nielsen, Ole Lund, Claus Lundegaard, 2006, Trends in biotechnology.

PopCover-2.0. Improved Selection of Peptide Sets With Optimal HLA and Pathogen Diversity Coverage

M. Nielsen, A. Sette, J. Nilsson, 2021, Frontiers in Immunology.

Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data

Sri H. Ramarathinam, M. Nielsen, J. Kringelum, 2019, Immunogenetics.

Evaluation of peptide selection approaches for epitope-based vaccine design.

M. Nielsen, O. Lund, M. Nielsen, 2013, Tissue antigens.

Identification of peptides from foot-and-mouth disease virus structural proteins bound by class I swine leukocyte antigen (SLA) alleles, SLA-1*0401 and SLA-2*0401.

M. Nielsen, S. Buus, M. Harndahl, 2013, Animal genetics.

PopCover: a method for selecting of peptides with optimal population and pathogen coverage

Morten Nielsen, Ole Lund, Claus Lundegaard, 2010, BCB '10.

MHC Class I Epitope Binding Prediction Trained on Small Data Sets

Søren Brunak, Morten Nielsen, Ole Lund, 2004, ICARIS.

Selecting Informative Data for Developing Peptide-MHC Binding Predictors Using a Query by Committee Approach

Søren Brunak, Morten Nielsen, Ole Lund, 2003, Neural Computation.

NetMHCphosPan - Pan-specific prediction of MHC class I antigen presentation of phosphorylated ligands

M. Nielsen, C. Barra, N. Ternette, 2021, ImmunoInformatics.

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

M. Nielsen, M. Andreatta, C. Barra, 2017, bioRxiv.

Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

M. Nielsen, S. Carrasco Pro, M Nielsen, 2014, Tissue antigens.

Next-generation ELISA diagnostic assay for Chagas Disease based on the combination of short peptidic epitopes

Santiago J. Carmona, M. Nielsen, Fernán Agüero, 2017, PLoS neglected tropical diseases.

Expanding specificity of class I restricted CD8+ T cells for viral epitopes following multiple inoculations of swine with a human adenovirus vectored foot-and-mouth disease virus (FMDV) vaccine.

M. Nielsen, S. Buus, L. E. Pedersen, 2016, Veterinary immunology and immunopathology.

Thiopurine 6TG treatment increases tumor immunogenicity and response to immune checkpoint blockade

M. Nielsen, S. Buus, C. Bertolotto, 2022, Oncoimmunology.

Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

Cristina Marino Buslje, Elin Teppa, Tomás Di Domenico, 2010, PLoS Comput. Biol..

NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors

Morten Nielsen, Massimo Andreatta, William H. Hildebrand, 2018, Cancer Immunology Research.

Large-scale identification of T cell epitopes derived from SARS-CoV-2 for the development of peptide vaccines against COVID-19.

M. Nielsen, Yipeng Ma, Tong Lin, 2021, The Journal of infectious diseases.

Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients

M. Nielsen, J. Mordoh, M. M. Barrio, 2023, Exploration of Immunology.

MISTIC: mutual information server to infer coevolution

Cristina Marino Buslje, Elin Teppa, Morten Nielsen, 2013, Nucleic Acids Res..

Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions

M. Nielsen, Bjoern Peters, J. Sidney, 2014, BMC Bioinformatics.

Benchmark datasets of immune receptor-epitope structural complexes

M. Nielsen, Bjoern Peters, A. Sette, 2019, BMC Bioinform..

CD4 T Cell Responses to Theileria parva in Immune Cattle Recognize a Diverse Set of Parasite Antigens Presented on the Surface of Infected Lymphoblasts

Olukemi O. Ifeonu, Joana C. Silva, M. Nielsen, 2021, The Journal of Immunology.

Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells

M. Nielsen, L. Jessen, A. Bentzen, 2023, eLife.

Data-driven filtering for denoising of TCRpMHC single-cell data: a benchmark

M. Nielsen, L. Jessen, Helle Rus Povlsen, 2023, bioRxiv.

NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions

M. Nielsen, L. Jessen, A. Montemurro, 2022, Frontiers in Immunology.

Integration of HLA-DR linkage disequilibrium to MHC class II predictions

M. Nielsen, C. Barra, P. W. Sackett, 2023, bioRxiv.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

M. Nielsen, C. Barra, Yuchen Li, 2023, bioRxiv.

TRAV1-2+ CD8+ T-cells including oligoconal expansions of MAIT cells are enriched in the airways in human tuberculosis

T. Ndung’u, M. Nielsen, W. Bishai, 2019, Communications Biology.

2019-0605 TRAV 1-2 ( + ) CD 8 ( + ) T-cells including oligoconal expansions of MAIT cells are enriched in the airways in human tuberculosis

M. Nielsen, W. Bishai, U. Lalloo, 2019 .

Breaking confinement: unconventional peptide presentation by major histocompatibility (MHC) class I allele HLA-A*02:01

M. Nielsen, B. Peters, Curtis McMurtrey, 2017 .

The structure of songbird MHC class I reveals antigen binding that is flexible at the N-terminus and static at the C-terminus

M. Nielsen, S. Buus, K. Lindkvist-Petersson, 2023, Frontiers in immunology.

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations

O. Lund, S. Brunak, C. Lundegaard, 2002 .

SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments

Morten Nielsen, Ole Lund, Ilka Hoof, 2013, Nucleic Acids Res..

MIToS.jl: mutual information tools for protein sequence analysis in the Julia language

Cristina Marino Buslje, Morten Nielsen, Diego J. Zea, 2016, Bioinform..

Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction

Angela D. Wilkins, Cristina Marino Buslje, Elin Teppa, 2012, BMC Bioinformatics.

Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations

O. Lund, S. Brunak, C. Lundegaard, 2003, Protein science : a publication of the Protein Society.

Machine Learning Competition in Immunology – Prediction of HLA class I molecules

Christos, M. Polycarpou, O. Lund, 2011 .

Characterization of Antigenic MHC-Class-I-Restricted T Cell Epitopes in the Glycoprotein of Ebolavirus

M. Nielsen, A. Zeltiña, T. Bowden, 2019, Cell reports.

TRAV1-2+ CD8+ T-cells including oligoconal expansions of MAIT cells are enriched in the airways in human tuberculosis

T. Ndung’u, M. Nielsen, W. Bishai, 2019, Communications Biology.

M. Nielsen, C. Mazzoni, I. Obara, 2016, Immunogenetics.

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification

M. Nielsen, S. Buus, M. Andreatta, 2015, Immunogenetics.

Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

O. Lund, C. Lundegaard, M. Nielsen, 2012, Expert review of vaccines.

Bioinformatics Tools for the Prediction of T-Cell Epitopes.

M. Nielsen, M. Andreatta, 2018, Methods in molecular biology.

SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments

O. Lund, M. Nielsen, I. Hoof, 2013, Nucleic Acids Res..

ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping

Morten Nielsen, Ole Lund, Paolo Marcatili, 2017, PloS one.

A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01

M. Nielsen, S. Buus, M. Harndahl, 2015, Immunogenetics.

The Effect of a Tropical Climate on Available Nutrient Resources to Springs in Ophiolite-Hosted, Deep Biosphere Ecosystems in the Philippines

M. Nielsen, Bjoern Peters, J. Sidney, 2017, Front. Microbiol..

Uncovering the Peptide-Binding Specificities of HLA-C: A General Strategy To Determine the Specificity of Any MHC Class I Molecule

M. Nielsen, S. Buus, M. Harndahl, 2014, The Journal of Immunology.

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

M. Nielsen, M. Andreatta, 2016, Bioinform..

Identification of the cognate peptide‐MHC target of T cell receptors using molecular modeling and force field scoring

M. Nielsen, P. Marcatili, Esteban Lanzarotti, 2018, Molecular immunology.

Machine learning reveals a non‐canonical mode of peptide binding to MHC class II molecules

M. Nielsen, Bjoern Peters, A. Sette, 2017, Immunology.

MHC-I Ligand Discovery Using Targeted Database Searches of Mass Spectrometry Data: Implications for T-Cell Immunotherapies.

S. Stevanović, M. Nielsen, D. Clements, 2017, Journal of proteome research.

Pan-Specific Prediction of Peptide–MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity

M. Nielsen, S. Buus, M. Harndahl, 2016, The Journal of Immunology.

Enhancing Mass Spectrometry-Based MHC-I Peptide Identification Through a Targeted Database Search Approach.

M. Nielsen, S. Gujar, Prathyusha Konda, 2019, Methods in molecular biology.

The MHC Motif Viewer: A Visualization Tool for MHC Binding Motifs

O. Lund, M. Nielsen, I. Hoof, 2010, Current protocols in immunology.

High-throughput sequencing enhanced phage display enables the identification of patient-specific epitope motifs in serum

O. Lund, N. Rigby, M. Nielsen, 2015, Scientific Reports.

Linear epitope mapping of peanut allergens demonstrates individualized and persistent antibody-binding patterns.

M. Nielsen, M. Dufva, Z. Szépfalusi, 2016, The Journal of allergy and clinical immunology.