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Gräf, L. ; Sens, D.W. ; Shilova, L. ; Casale, F.P.

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)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Research in Computational Molecular Biology
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14758 LNCS, Heft: , Seiten: 385-389 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Institute of Computational Biology (ICB)
Förderungen Free State of Bavaria's Hightech Agenda through the Institute of AI for Health (AIH)
Friedrich-Alexander-Universität Erlangen-Nürnberg