Birkir Reynisson

PhD Student, Department of Health Technology
Bioinformatics
Immunoinformatics and Machine Learning

Emailbirey@dtu.dk
Websitehttp://www.healthtech.dtu.dk

Kemitorvet, 204, 255 -
2800 Kgs. Lyngby
Denmark

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5 Journal article

Improved prediction of HLA antigen presentation hotspots: applications for immunogenicity risk assessment of therapeutic proteins
Attermann, A. S., Barra, C., Reynisson, B., Schultz, H. S., Leurs, U., Lamberth, K. & Nielsen, M., 2021, (Accepted/In press) In : Immunology.
Research output: Contribution to journal › Journal article › Research › peer-review
Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
Barra, C., Ackaert, C., Reynisson, B., Schockaert, J., Jessen, L. E., Watson, M., Jang, A., Comtois-Marotte, S., Goulet, J-P., Pattijn, S., Paramithiotis, E. & Nielsen, M., 2020, In : Frontiers in Immunology. 11, 13 p., 1304.
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File
46 Downloads (Pure)
Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data
Reynisson, B., Barra, C., Kaabinejadian, S., Hildebrand, W. H., Peters, B. & Nielsen, M., 2020, In : Journal of Proteome Research. 19, 6, p. 2304-2315
Research output: Contribution to journal › Journal article › Research › peer-review
NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data
Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M., 2020, In : Nucleic acids research. 48, 1, p. W449-W454
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File
29 Downloads (Pure)
NNAlign_MA; MHC peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions
Alvarez, B., Reynisson, B., Barra, C., Buus, S., Ternette, N., Connelley, T., Andreatta, M. & Nielsen, M., 2019, In : Molecular and Cellular Proteomics. 18, 12, p. 2459-2477
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File
130 Downloads (Pure)

2 Active

T Cell Immunoinformatics
Reynisson, B., Nielsen, M. & Marcatili, P.
Samfinansieret - Andet
01/04/2018 → 31/03/2021
Project: PhD
A personalized approach for increased clinical efficacy of cancer immuneotherapy
Reynisson, B., Nielsen, M., Hadrup, S. R. & Svane, I. M.
01/04/2018 → 31/03/2021
Project: Research

Improved prediction of HLA antigen presentation hotspots: applications for immunogenicity risk assessment of therapeutic proteins

Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction

Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

NNAlign_MA; MHC peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

T Cell Immunoinformatics

A personalized approach for increased clinical efficacy of cancer immuneotherapy