2023 - Research.com Immunology in Denmark Leader Award
2022 - Research.com Immunology in Denmark Leader Award
His scientific interests lie mostly in Epitope, Major histocompatibility complex, Computational biology, Genetics and Peptide binding. Morten Nielsen has researched Epitope in several fields, including Protein structure, Cytotoxic T cell and Sequence analysis. His Major histocompatibility complex study incorporates themes from Peptide sequence and Peptide.
His Computational biology study combines topics in areas such as Molecular biology, Plasma protein binding, Immune system and Bioinformatics. His research investigates the connection with Genetics and areas like Binding selectivity which intersect with concerns in Rhesus macaque. The concepts of his Peptide binding study are interwoven with issues in HLA-DR, MHC class II and Artificial intelligence.
Morten Nielsen focuses on Epitope, Computational biology, Major histocompatibility complex, Human leukocyte antigen and MHC class I. In his study, Immunology is strongly linked to Cytotoxic T cell, which falls under the umbrella field of Epitope. His Computational biology study integrates concerns from other disciplines, such as Immunogenicity, Molecular biology, Sequence motif, In silico and Peptide.
His Major histocompatibility complex study is concerned with the field of Genetics as a whole. His study brings together the fields of CD8 and Human leukocyte antigen. His primary area of study in MHC class I is in the field of MHC restriction.
His main research concerns Computational biology, Epitope, T cell, Major histocompatibility complex and Immune system. The Computational biology study combines topics in areas such as Proteome, Immunogenicity, MHC class I, Peptide binding and Peptide. His Epitope research is within the category of Antigen.
Morten Nielsen usually deals with T cell and limits it to topics linked to Cytotoxic T cell and Immunology, Phenotype and Cancer research. His study in the field of MHC class II and MHC class II antigen is also linked to topics like Ligand. His work carried out in the field of Human leukocyte antigen brings together such families of science as In silico and Epitope mapping.
His scientific interests lie mostly in Computational biology, Major histocompatibility complex, T cell, Epitope and Antigen presentation. His research integrates issues of Transcriptome, Genome, Gene, Disease and Omics in his study of Computational biology. His Major histocompatibility complex study necessitates a more in-depth grasp of Immune system.
His T cell research is multidisciplinary, relying on both Cytotoxic T cell, Ovarian cancer, Tumor-infiltrating lymphocytes and Cell therapy. His research in Epitope intersects with topics in Receptor, Web server and Set. His work focuses on many connections between Ligand and other disciplines, such as MHC class II, that overlap with his field of interest in Peptide binding and Antigen processing.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19.
Takuya Sekine;André Perez-Potti;Olga Rivera-Ballesteros;Kristoffer Strålin.
Improved method for predicting linear B-cell epitopes.
Jens Erik Pontoppidan Larsen;Ole Lund;Morten Nielsen.
Immunome Research (2006)
Reliable prediction of T-cell epitopes using neural networks with novel sequence representations
Morten Nielsen;Claus Lundegaard;Peder Worning;Sanne Lise Lauemøller.
Protein Science (2003)
Sortilin is essential for proNGF-induced neuronal cell death
Anders Nykjaer;Ramee Lee;Kenneth K. Teng;Pernille Jansen;Pernille Jansen.
NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
Vanessa Isabell Jurtz;Sinu Paul;Massimo Andreatta;Paolo Marcatili.
Journal of Immunology (2017)
BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
Martin Closter Jespersen;Bjoern Peters;Morten Nielsen;Paolo Marcatili.
Nucleic Acids Research (2017)
NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11
Claus Lundegaard;Kasper Lamberth;Mikkel Harndahl;Søren Buus.
Nucleic Acids Research (2008)
NetMHCpan, a method for MHC class I binding prediction beyond humans
Ilka Hoof;Bjoern Peters;John Sidney;Lasse Eggers Pedersen.
A generic method for assignment of reliability scores applied to solvent accessibility predictions
Bent Petersen;Thomas Nordahl Petersen;Pernille Andersen;Pernille Andersen;Morten Nielsen.
BMC Structural Biology (2009)
Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction
Mette V Larsen;Claus Lundegaard;Kasper Lamberth;Soren Buus.
BMC Bioinformatics (2007)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: