Author

Bernd Bischl

Munich Center for Machine LearningORCID 0000-0001-6002-6980Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Assay:proteomics· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Benjamin SchubertiD· 2 shared Emilio DorigattiiD· 2 shared Adrian StraubiD· 1 shared Bolei MaiD· 1 shared Dirk Hans BuschiD· 1 shared Felix DrostiD· 1 shared Ingo Ziegler· 1 shared Karolin I. WagneriD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersBioinformatics

Benjamin SchubertORCID

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2 papers

Recent·Most cited

Predicting T Cell Receptor Functionality against Mutant Epitopes

Felix Drost, Emilio Dorigatti, Adrian Straub, Philipp Hilgendorf, Karolin I. Wagner, Kersten Heyer, Marta López Montes, Bernd Bischl, Dirk Hans Busch, Kilian Schober, Benjamin Schubert

P-TEAM predicts the effect of epitope mutations on T cell functionality using a Random Forest model trained on murine and human TCR-epitope interactions.

Cell Genomics · Aug 15, 2024Peer-reviewedBioinformatics

Proteasomal cleavage prediction: state-of-the-art and future directions

Ingo Ziegler, Bolei Ma, Bernd Bischl, Emilio Dorigatti, Benjamin Schubert

This paper reviews proteasomal cleavage prediction methods, benchmarks deep learning architectures, and finds that dataset quality limits performance more than model choice.

bioRxiv · Jul 17, 2023 · PreprintBioinformatics