The Quantitative Omics Epidemiology Group ("Quant Group") is a collaborative research group established to advance the application of statistical and quantitative omics epidemiology to biomedical data. The group integrates expertise in quantitative methods, biological interpretation, and clinical application to ensure that analyses remain biologically informed and clinically relevant.
The group is committed to bridging quantitative analysis with an understanding of underlying biological mechanisms, recognising that critical domain knowledge is essential for building robust and interpretable models, including those using artificial intelligence.
Membership is collaborative and flexible, comprising invited researchers contributing to specific projects, publications, and shared resources. Leadership and overall coordination remain with the founder. Membership is project-based and flexible, with researchers joining for individual collaborations and invited as authors on any resulting publications. Members are not subject to formal obligations or restrictions beyond standard authorship practices.
Authorship follows standard academic practices, with contributing members individually named as authors. The “Quant Group” may additionally be listed as a collective author to represent shared contributions, particularly in cases where no clear first, shared, or senior author is designated. In such instances, the phrase “on behalf of the Quant Group” may be used. The “Quant Group” authorship will only be included when agreed upon by all members contributing to that project.
Members come from institutions including:
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QuantBayes: A Bayesian model for quantifying genomic variant evidence sufficiency in Mendelian disease (2025). DOI: 10.1101/2025.12.02.25341503
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QuantCalc: Quantifying prior probabilities for disease-causing variants reveals the top genetic contributors in inborn errors of immunity (2025). DOI: 10.1101/2025.03.25.25324607
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PanelAppRex: PanelAppRex aggregates disease gene panels and facilitates sophisticated search Quant Group, Simon Boutry, Ali Saadat, Sinisa Savic, Luregn J. Schlapbach, Jacques Fellay, Dylan Lawless. Bioinformatics Advances (2026). DOI: 10.1093/bioadv/vbag115
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2025-10-23: Quant Group, Simon Boutry, Ali Saadat, Maarja Soomann, Johannes Trück, D. Sean Froese, Jacques Fellay, Sinisa Savic, Luregn J. Schlapbach, Dylan Lawless. A century-old problem in genetics solved by big data, Bayesian inference, and AI-driven diagnostics in SwissPedHealth. FZK/CRC annual retreat, Zürich.
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2025-09-19: Quant Group, Simon Boutry, Ali Saadat, Maarja Soomann, Johannes Trück, D. Sean Froese, Jacques Fellay, Sinisa Savic, Luregn J. Schlapbach, Dylan Lawless. Quantifying variant evidence for precise, probabilistic genomics. Computational Biology Symposium, Lausanne.
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