Author

Michael Bitzer

German Cancer Research CenterORCID 0000-0002-4463-8263Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

Gisela GabernetiD· 2 shared Adrian Seyboldt· 1 shared Albrecht StenzingeriD· 1 shared Alexander BartholomäusiD· 1 shared Alexander DiltheyiD· 1 shared Alexander GoesmanniD· 1 shared Amirhossein SakhtemaniD· 1 shared Andreas DiefenbachiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBioinformatics

Gisela GabernetORCID

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Author profile →

3 papers

Recent·Most cited

Prospective pan-cancer phosphoproteomics at clinical scale extends therapeutic options in precision oncology

Annika Schneider, Julia Wortmann, Cecilia Bang Jensen, Leonardo Estrada Duenas, Maria‐Veronica Teleanu, Amirhossein Sakhteman, Firas Hamood, Florian P Bayer, Christoph Stange, Jeremiah Bintang Santoso, Jennifer Hüellein, Nindo B. Punturi, Lee Dolat, Peter Horak, Resch Moritz, , Bernhard Küster

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

bioRxiv · Jul 9, 2026 · PreprintCancer Biology

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Stefanie Warnat‐Herresthal, Hartmut Schultze, Krishnaprasad Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N. Ahmad Aziz, Sofia Ira Ktena, Florian Tran, Michael Bitzer, Stephan Ossowski, Nicolas Casadei, , Alexander Goesmann

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

Nature · May 26, 2021Peer-reviewedBioinformatics

Efficient data management infrastructure for the integration of imaging and omics data in life science research

Luis Kuhn Cuellar, Andreas Friedrich, Gisela Gabernet, Luis de la Garza, Sven Fillinger, Adrian Seyboldt, Sven zur Oven‐Krockhaus, Friederike Wanke, Sandra Richter, Wolfgang Maximilian Thaiss, Marius Horger, Nisar P. Malek, Klaus Harter, Michael Bitzer, Sven Nahnsen

The paper proposes a FAIR-compliant data management system integrating imaging and omics data using Service Oriented Architecture principles, demonstrating its use in plant biology and hepatocellular carcinoma studies.

bioRxiv · Dec 28, 2019 · PreprintBioinformatics