Author

Paul Simon

Lyon 1 Université

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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Bioinformatics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Assay:GWAS summary statistics· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

Alena KrajčoviechováiD· 1 shared Anthony RodgersiD· 1 shared Arnaud BonnaffouxiD· 1 shared Bérangère Robert· 1 shared Bryan WilliamsiD· 1 shared Camil Hishmih· 1 shared Candan Hızel· 1 shared Carole Long· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Alena KrajčoviechováORCID

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Author profile →

2 papers

Recent·Most cited

Inference and validation of a dynamic gene regulatory network influencing cell fate during Duchenne muscular dystrophy initiation

Elsa Gratianne Guillot, Quentin Fort, Elise Mozin, Bérangère Robert, Matteo Bouvier, Cynthia Fourgeux, Paul Simon, Sara Munoz, Matthew Carter Childers, Jérémie Poschmann, Oumeya Adjali, David L. Mack, Arnaud Bonnaffoux, Jean-Baptiste Dupont

The paper identifies a dynamic gene regulatory network in Duchenne muscular dystrophy that controls cell fate decisions and shows how disease mutations disrupt this network.

bioRxiv · Nov 19, 2025 · PreprintBioinformatics

Polygenic risk scores predict diabetic complications and their response to therapy

Johanne Tremblay, Mounsif Haloui, Redha Attaoua, Muhammad Ramzan Tahir, Camil Hishmih, François Harvey, François-Christophe Marois-Blanchet, Carole Long, Paul Simon, Lara Santucci, Candan Hızel, John Philip Chalmers, Michel Marre, Stephen B. Harrap, Renata Cífková, , Pavel Hamet

This paper develops a polygenic risk score model to predict diabetic complications and their response to therapy using genome-wide association study data.

Diabetologia · Jul 5, 2021Peer-reviewedGenetic & Genomic Medicine