Author

Günther Schmalzing

Universitätsklinikum Aachen

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

Biology Disciplines

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Neuroscience· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Ralf HausmanniD· 2 shared Abhilasha Ojha· 1 shared Alexander BartholomäusiD· 1 shared Alexander DiltheyiD· 1 shared Alexander GoesmanniD· 1 shared Alison G. ColeiD· 1 shared Andreas DiefenbachiD· 1 shared Andreas KelleriD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersBioinformatics

Ralf HausmannORCID

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Author profile →

3 papers

Recent·Most cited

Functional analysis of ligand-gated chloride channels in a cnidarian sheds light on the evolution of inhibitory signalling

Abhilasha Ojha, Linda Kloss, Juan D. Montenegro, Simone Albani, Audrey Ortega-Ramírez, Mihaela Raycheva, Sylvia Joussen, Sabrina Kaul, Michèle Bachmann, Lisa Huf, Günther Schmalzing, Alison G. Cole, Ulrich Technau, Stefan Gründer

The paper functionally analyzes ligand-gated chloride channels in cnidarians, finding that some use glutamate instead of GABA for inhibitory signaling, suggesting early diversification of inhibitory receptors.

Advanced Science · May 1, 2026Peer-reviewedNeuroscience

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

Uncoupling sodium channel dimers rescues phenotype of pain-linked Nav1.7 mutation

Annika H. Rühlmann, Jannis Körner, Ralf Hausmann, Nikolay Bebrivenski, Christian Neuhof, Silvia Detro‐Dassen, Petra A. I. Hautvast, Carène Benasolo, Jannis Meents, Jan‐Philipp Machtens, Günther Schmalzing, Angelika Lampert

The paper shows that uncoupling dimerized Nav1.7 channels reduces persistent current caused by a pain mutation, suggesting dimerization plays a role in pain syndromes.

British Journal of Pharmacology · Jul 14, 2020Peer-reviewedNeuroscience