Author

A. B. Riemer

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Immunology· 2 Biochemistry· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:experimental assay· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:drug response / drug discovery· 2 Research Focus:host-pathogen & immune response· 2 Assay:proteomics· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1

Frequent Co-Authors

M. Bonsack· 2 shared M. Salek· 2 shared A. Ahadova· 1 shared A. Ballhausen· 1 shared A. Benner· 1 shared A. Omdahl· 1 shared B. Kaminow· 1 shared C. Lotsch· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersImmunology

M. Bonsack

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

optiPRM: A targeted immunopeptidomics LC-MS workflow with ultra-high sensitivity for the detection of mutation-derived tumor neoepitopes from limited input material

M. Salek, J. D. Forster, J. P. Becker, M. Meyer, P. Charoentong, Y. Lyu, K. Lindner, C. Lotsch, M. Volkmar, F. Momburg, I. Poschke, S. Frohling, M. Schmitz, R. Offringa, M. Platten, , A. B. Riemer

The paper introduces optiPRM, a targeted LC-MS workflow that enhances sensitivity for detecting mutation-derived neoepitopes from limited tumor samples.

Molecular & Cellular Proteomics · Sep 1, 2024Peer-reviewedImmunology

Light contamination in stable isotope-labelled internal peptide standards is frequent and a potential source of false discovery and quantitation error in proteomics

M. Salek, J. D. Foerster, W.-D. Lehmann, A. B. Riemer

The paper identifies light contamination in heavy labelled synthetic peptides as a source of false discovery and quantitation error in proteomics.

bioRxiv · Nov 8, 2021Biochemistry

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

X. M. Shao, R. Bhattacharya, J. Huang, I. K. A. Sivakumar, C. Tokheim, L. Zheng, D. HIrsch, B. Kaminow, A. Omdahl, M. Bonsack, A. B. Riemer, V. Velculescu, V. Anagnostou, K. Pagel, R. Karchin

The paper introduces MHCnuggets, a deep learning method for predicting MHC class I and II neoantigen binding, showing improved performance and scalability across 26 cancer types.

bioRxiv · Oct 4, 2019Immunology

The shared neoantigen landscape of MSI cancers reflects immunoediting during tumor evolution

A. Ballhausen, M. J. Przybilla, M. Jendrusch, S. Haupt, E. Pfaffendorf, M. Draxlbauer, F. Seidler, S. Krausert, A. Ahadova, M. S. Kalteis, D. Heid, J. Gebert, M. Bonsack, S. Schott, H. Bläker, , M. Kloor

The study identifies shared immunogenic neoantigens in MSI cancers and finds a negative correlation between indel mutation frequency and neoantigen immunogenicity, suggesting immunoediting during tumor evolution.

bioRxiv · Jul 16, 2019Cancer Biology