Computational modelling

Publications

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models

Haykanush Ohanyan , Mark van de Wiel, Lützen Portengen, Alfred Wagtendonk, Nicolette R. den Braver, Trynke R. de Jong, Monique Verschuren, Katja van den Hurk, Karien Stronks, Eric Moll van Charante, Natasja M. van Schoor, Coen D.A. Stehouwer, Anke Wesselius, Annemarie Koster, Margreet ten Have, Brenda W.J.H. Penninx, Marieke F. van Wier, Irina Motoc, Albertine J. Oldehinkel, Gonneke Willemsen, Dorret I. Boomsma, Mariëlle A. Beenackers, Anke Huss, Martin van Boxtel, Gerard Hoek, Joline W.J. Beulens, Roel Vermeulen, and Jeroen Lakerveld
Environmental Health Perspectives, Volume 132, Issue 6 (2024)

Validating and constructing behavioral models for simulation and projection using automated knowledge extraction

Tabea S. Sonnenschein, G. Ardine de Wit, Nicolette R. den Braver, Roel C.H. Vermeulen, Simon Scheider
Information Sciences Volume 662 (2024)

Machine learning approaches to characterize the obesogenic urban exposome

Haykanush Ohanyan, Lützen Portengren, Anke Huss, Eugenio Traini, Joline W.J. Beulens, Gerard Hoek, Jeroen Lakerveld, Roel Vermeulen
Environment International (158), Jan 2022

Decoding the exposome

Decoding the exposome

The environment we live in has a dominant impact on our health. It explains an estimated seventy percent of the chronic disease burden. Where we live, what we eat, how much we exercise, the air we breathe and whom we associate with; all of these environmental factors play a role. The combination of these factors over the life course is called the exposome. There is general (scientific) consensus that understanding more about the exposome will help explain the current burden of disease and that it provides entry points for prevention and ...

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