Author

Stefan Solbrig

University of RegensburgORCID 0009-0002-0338-3142Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:gene regulatory network inference· 3 Organism:human· 2 Approach:statistical model· 2 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

Helena U. ZachariasiD· 2 shared Michael AltenbuchingeriD· 2 shared Peter Josef OefneriD· 2 shared Rainer SpangiD· 2 shared Andreas Schäfer· 1 shared Annette M. StaigeriD· 1 shared Bence Oláh· 1 shared Dennis VoelkliD· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersSystems Biology

Helena U. ZachariasORCID

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Author profile →

3 papers

Recent·Most cited

PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

Robin Kosch, Katharina Limm, Annette M. Staiger, Nadine S. Kurz, Nicole Seifert, Bence Oláh, Stefan Solbrig, Marita Ziepert, Emil Chteinberg, Rainer Spang, Reiner Siebert, Helena U. Zacharias, German Ott, Peter Josef Oefner, Michael Altenbuchinger

PriOmics integrates proteomics with other omics data using graphical models to better understand relationships between proteins and their modifications.

bioRxiv · Nov 10, 2023 · PreprintSystems Biology

SpaCeNet: Spatial Cellular Networks from omics data

Stefan Schrod, Niklas Lück, Robert Lohmayer, Stefan Solbrig, Dennis Voelkl, Tina Wipfler, Katherine Hoff Shutta, Marouen Ben Guebila, Andreas Schäfer, Tim Beißbarth, Helena U. Zacharias, Peter Josef Oefner, John F Quackenbush, Michael Altenbuchinger

SpaCeNet identifies intracellular and intercellular molecular networks from spatial omics data by analyzing conditional independence relations between variables within and across cells.

bioRxiv · Sep 1, 2022 · PreprintBioinformatics

Modelling cancer progression using Mutual Hazard Networks

Rudolf Schill, Stefan Solbrig, Tilo Wettig B, Rainer Spang

This paper introduces Mutual Hazard Networks to model cancer progression from cross-sectional data, finding that IDH1 mutations promote TP53 mutations in glioblastoma.

Bioinformatics · Jun 25, 2019Peer-reviewedBioinformatics