Mark van de Wiel

Affiliation
Professor in Statistics for high-dimensional (omics) data
Dep. Epidemiology and Data Science
Amsterdam University Medical Center, location VUmc, Amsterdam, NL
Visiting fellow MRC Biostatistics Unit, Cambridge University, UK
mark.vdwiel [at] vumc.nl
www.bigstatistics.nl/mark-van-de-wiel/
Twitter: @MarkvandeWiel4
LinkedIn


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, omics integration modeling and deconvolution of omics profiles. 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

Cambridge, November 2021 (Armitage workshop): Improving prediction, variable selection and treatment effect estimation by the adaptive, multi-penalty elastic net
Milan, April 2018: Improving high-dimensional prediction by empirical Bayes learning from co-data

Selected publications (Full list: Google Scholar; Web-of-Science)

Andrade Barbosa B, ..., Van de Wiel MA*, Kim, Y* (2021). Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data. Nature communications, 12, 1-13.

van Nee, Mirrelijn M, Lodewyk FA Wessels, and Mark A. van de Wiel. Flexible co‐data learning for high‐dimensional prediction. Statistics in medicine 40.26 (2021): 5910-5925.

Rauschenberger, Armin, Enrico Glaab, and Mark A. van de Wiel. Predictive and interpretable models via the stacked elastic net. Bioinformatics 37.14 (2021): 2012-2016.

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. 

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.

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 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.


Teaching
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. I also teach Biostatistics to medicine students in the research master Oncology/CVD.


Media
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