Mark van de Wiel

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 is omics-based clinical prediction and classification. Here, I focus on developing methods to improve predictive performance and biomarker selection by structural use of auxiliary 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.

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

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)

van de Wiel MA, te Beest DE, Münch M (2018). Learning from a lot: Empirical Bayes in high-dimensional model-based prediction settings. Scand J Stat. arXiv preprint arXiv:1709.04192.

Leday GGR, de Gunst M, Kpogbezan GB, Van der Vaart AW, Van Wieringen, WN, & Van de Wiel MA (2016). Gene network reconstruction using global-local shrinkage priors. Ann Appl Statist.

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)

Scheinin I, Sie D, Bengtsson H, van de Wiel MA, Olshen AB, van Thuijl HF, van Essen HF, Eijk PP, Rustenburg F, Meijer GA, Reijneveld JC, Wesseling P, Pinkel D, Albertson DG, Ylstra B. (2014). DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res24:2022-32.

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. Thorax69:451-7

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.

Wilting SM, Snijders PJ, Verlaat W, Jaspers A, van de Wiel MA, van Wieringen WN, Meijer GA, Kenter GG, Yi Y, le Sage C, Agami R, Meijer CJ, Steenbergen RD (2013). Altered microRNA expression associated with chromosomal changes contributes to cervical carcinogenesis. Oncogene, 32:106-16.


Personal web site
I maintain a personal web site with additional information on research, contributions in popular media, software and teaching.