Author

Marian Breuer

Maastricht UniversityORCID 0000-0002-0529-4031Unclaimed 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 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Michiel E. AdriaensiD· 2 shared Bastien NihantiD· 1 shared Chaitra SarathyiD· 1 shared Chris T. EveloiD· 1 shared Cyril LaggeriD· 1 shared Eva Thielecke· 1 shared Ilja C.W. ArtsiD· 1 shared João Pedro de MagalhãesiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersSystems Biology

Michiel E. AdriaensORCID

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3 papers

Recent·Most cited

Systems BiologyCirculation Genomic and Precision MedicinePeer-reviewedJul 3, 2026

Metabolic task analysis reveals distinct metabotypes in end-stage dilated cardiomyopathy

Bastien Nihant, Job A.J. Verdonschot, Sonia Balăn…·Michiel E. Adriaens

The paper identifies two distinct metabotypes in end-stage DCM patients using transcriptomics data and metabolic modeling, revealing metabolic and immune-related differences.

DOI 10.1161/circgen.125.005366

humanbulk RNA-seq

BioinformaticsbioRxivPreprintOct 3, 2024

Mosaic Regulation of Stress Pathways Underlies Senescent Cell Heterogeneity

Roberto A. Avelar, Thomas Duffield, Cyril Lagger…·João Pedro de Magalhães

The paper identifies senescent cell heterogeneity by analyzing stress response enrichment in bulk RNA-seq data and proposes a model based on Stress Response Modules.

5 citations

humanbulk RNA-seq

Systems BiologybioRxivPreprintSep 14, 2020

ComMet: A method for comparing metabolic states in genome-scale metabolic models

Chaitra Sarathy, Marian Breuer, Martina Kutmon…·Ilja C.W. Arts

ComMet is a method for analyzing and comparing metabolic flux states in genome-scale models, identifying distinct network modules and differences in human adipocyte metabolism under varying amino acid uptake conditions.

DOI 10.1101/2020.09.14.296145

humanmetabolomics