Mark van de Wiel

Professor in Statistics for Genomics
Dep. Epidemiology & Biostatistics
Amsterdam University Medical Center, location VUmc, Amsterdam, NL
Visiting fellow MRC Biostatistics Unit, Cambridge University, UK
mark.vdwiel [at]
Twitter: @MarkvandeWiel4

Research statement
Data drives most of my statistical omics research: provide a generic, robust solution for a given study, and one likely solves similar problems for many studies. My research interests cover a wide spectrum, including differential expression (multiple) testing, network estimation and omics integration modeling. My main fascination nowadays is omics-based clinical prediction and classification, by either statistical or machine learners. Here, I focus on developing methods to improve predictive performance and biomarker selection by structural use of complementary data (co-data), e.g. from external studies or data bases. We directly apply and test such methods in a number of collaborative projects on cancer diagnostics and prognostics.

Selected presentations
Milan, April 2018: Improving high-dimensional prediction by empirical Bayes learning from co-data
Barcelona, March 2018: Improving high-dimensional prediction by empirical Bayes learning from co-data
Baltimore, July 2017: Empirical Bayes learning from co-data in high-dimensional prediction settings

Selected publications (Full list: Google Scholar; Web-of-Science)
van de Wiel MA, te Beest DE, Münch M (2019). Learning from a lot: Empirical Bayes in high-dimensional model-based prediction settings. Scand J Stat. 46, 2-25. (Open Access)

Snoek BC, Verlaat W, Novianti PW, Van de Wiel MA, Wilting SM, van Trommel NE, Bleeker MCG, . Massuger LFAG, Melchers WJG, Sie D, Heideman DAM, Snijders PJF, Meijer CJLM, Steenbergen RDM (2018). Genome-wide microRNA analysis of HPV-positive self-samples yields novel 1 triage markers for early detection of cervical cancer. Int J Cancer. 144, 372-379.

Te Beest DE, Mes SW, Wilting SM, Brakenhoff RH, Van de Wiel MA (2017). Improved high-dimensional prediction with Random Forests by the use of co-data. BMC Bioinformatics, 18, 584.

Leday GGR, de Gunst M, Kpogbezan GB, Van der Vaart AW, Van Wieringen, WN, & Van de Wiel MA (2017). Gene network reconstruction using global-local shrinkage priors. Ann Appl Statist. 11, 41-68.

Van de Wiel MA, Lien TG, Verlaat W, Van Wieringen WN, Wilting SM (2016). Better prediction by use of co-data: Adaptive group-regularized ridge regression. Stat Med. 35, 368-381. Preliminary version (arXiv)

Van Boerdonk RA, Daniels JM, Snijders PJ, Grünberg K, Thunnissen E, van de Wiel MA, Ylstra B, Postmus PE, Meijer CJ, Meijer GA, Smit EF, Sutedja TG, Heideman DA (2014). DNA copy number aberrations in endobronchial lesions: a validated predictor for cancer. Thorax. 69:451-7

Van de Wiel MA, Leday GGR, Pardo L, Rue H, Van der Vaart AW, Van Wieringen WN (2013). Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. Freely available as Top 10 Cited. Biostatistics, 14, 113-128.

R Packages
We want our methods to be used, so we implemented these in R-packages, which include data, example(s) and documentation. Group packages.

Together with Wessel van Wieringen, I teach High-Dimensional Data Analysis in the Statistical Science Master, Leiden. Topics involve: regularized regression, multiple testing, shrinkage, empirical Bayes, analysis of high-dimensional count data. At AUMC, our group organises a bi-yearly 4-day post-graduate course on Statistics for Omics. This course mostly targets PhD-students and PostDocs with a (molecular) biology background and basic skills in R. Check for dates.

Interview/Comments on Personalized Medicine in the 'Trouw' (Dutch newspaper), 09/12/2017
Nieuwsbericht honorering ZONMW TOP subsidie voor project "Compute CANCER", February 2017.
Contribution 'Nieuw Archief voor de Wiskunde': "Statistiek op het genoom: ‘Big Data’, maar dan anders", December 2015
Interview VOZ magazine, guest-edited by Wouter Bos, July 2015
Interview in the 'Volkskrant' (Dutch newspaper), 23/08/2014