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Open Access Green as soon as Postprint is submitted to ZB.
Disease risk predictions with differentiable mendelian randomization.
In: (Research in Computational Molecular Biology). Berlin [u.a.]: Springer, 2024. 385-389 (Lect. Notes Comput. Sc. ; 14758 LNCS)
Predicting future disease onset is crucial in preventive healthcare, yet longitudinal datasets linking early risk factors to subsequent health outcomes are scarce. To address this challenge, we introduce Differentiable Mendelian Randomization (DMR), an extension of the classical Mendelian Randomization framework for disease risk predictions without longitudinal data. To do so, DMR leverages risk factors and genetic profiles from a healthy cohort, along with results from genome-wide association studies (GWAS) of diseases of interest. In this work, we describe the DMR framework and confirm its reliability and effectiveness in simulations and an application to a type 2 diabetes (T2D) cohort.
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Publication type
Article: Conference contribution
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
Research in Computational Molecular Biology
Quellenangaben
Volume: 14758 LNCS,
Pages: 385-389
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute of AI for Health (AIH)
Institute of Computational Biology (ICB)
Institute of Computational Biology (ICB)
Grants
Free State of Bavaria's Hightech Agenda through the Institute of AI for Health (AIH)
Friedrich-Alexander-Universität Erlangen-Nürnberg
Friedrich-Alexander-Universität Erlangen-Nürnberg