Author

Benjamin Krämer

Case Western Reserve UniversityORCID 0000-0002-1385-9709Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Physiology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:cell type annotation· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Organism:rat· 1 Approach:statistical model· 1

Frequent Co-Authors

Agustin Gonzalez‐VicenteiD· 1 shared Alexander BartholomäusiD· 1 shared Alexander DiltheyiD· 1 shared Alexander GoesmanniD· 1 shared Andreas DiefenbachiD· 1 shared Andreas KelleriD· 1 shared Angel AngeloviD· 1 shared Anke BeckeriD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperPhysiology

Agustin Gonzalez‐VicenteORCID

Author profile →

2 papers

Recent·Most cited

Abnormal activation of the mineralocorticoid receptor in the aldosterone-sensitive distal nephron contributes to fructose-induced salt-sensitive hypertension

Ronghao Zhang, Najeong Kim, Shujie Shi, Darshan Aatmaram Jadhav, Autumn R. Brostek, Beau R. Forester, Rashi Shukla, Christina Qu, Benjamin Krämer, Fraser J. Moss, Jeffrey Lawrence Garvin, Thomas R. Kleyman, Agustin Gonzalez‐Vicente

This study shows fructose-induced salt-sensitive hypertension is linked to abnormal MR activation in the ASDN, increasing ENaC expression and Na+ retention.

American Journal of Physiology-Renal Physiology · Jul 11, 2026Peer-reviewedPhysiology

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