Projects per year
Personal profile
Ancillary activities
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Ancillary activities are updated daily
specialisation
Genomics Data Analysis | Machine Learning | Statistical Inference | Bayesian Methods | Clinical Prediction Modeling
Research interests
- Analysis of high-dimensional data, mostly genomics
- Machine learning with small sample size
- Statistical inference (testing, confidence intervals, etc)
- Application and development of Bayesian methods for medical data
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 high-dimensional data analysis (omics) and predictive modeling, incl. machine learning. 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. Moreover, we develop tools to aid interpretation of ML, e.g. by providing inference for variable importance metrics. We directly apply and test such methods in a number of collaborative projects on cancer diagnostics and prognostics.
Activities
- Teaching: Biostatistics topics in several medical tracks (VU University) and High-dimensional data analysis in the Statistics and Data Science Master programme (Leiden University)
- Consult: Supporting Amsterdam UMC medical researchers, with a focus on analysis of omics data and machine learning
Collaborations and top research areas from the last five years
Projects
- 1 Active
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Exposome-NL
Mackenbach, J., Lakerveld, J., Beulens, J., van de Wiel, M., Abreu, T., Siddiqui, N. & den Braver, N.
1/01/2020 → 31/12/2029
Project: Research
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A BAYESIAN ACCELERATED FAILURE TIME MODEL FOR INTERVAL CENSORED THREE-STATE SCREENING OUTCOMES
Klausch, T., Akwiwu, E. U., VAN DE WIEL, M. A., Coupé, V. M. H. & Berkhof, J., 2023, In: Annals of Applied Statistics. 17, 2, p. 1285-1306 22 p.Research output: Contribution to journal › Article › Academic › peer-review
1 Citation (Scopus) -
ecpc: an R-package for generic co-data models for high-dimensional prediction
van Nee, M. M., Wessels, L. F. A. & van de Wiel, M. A., Dec 2023, In: BMC Bioinformatics. 24, 1, p. 172 172.Research output: Contribution to journal › Article › Academic › peer-review
Open Access -
Identifiability of the random effects’ covariance matrix of the linear mixed model
Amestoy, M., van de Wiel, M. A. & van Wieringen, W. N., 2023, (E-pub ahead of print) In: Communications in Statistics - Theory and Methods.Research output: Contribution to journal › Article › Academic › peer-review
Open Access -
Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma
Boot, P. A., Mes, S. W., de Bloeme, C. M., Martens, R. M., Leemans, C. R., Boellaard, R., van de Wiel, M. A. & de Graaf, P., 1 Feb 2023, In: Oral Oncology. 137, 106307.Research output: Contribution to journal › Article › Academic › peer-review
Open Access5 Citations (Scopus) -
Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma
Roemer, M. G. M., van de Brug, T., Bosch, E., Berry, D., Hijmering, N., Stathi, P., Weijers, K., Doorduijn, J., Bromberg, J., van de Wiel, M., Ylstra, B., de Jong, D. & Kim, Y., 18 Aug 2023, In: iScience. 26, 8, 107331.Research output: Contribution to journal › Article › Academic › peer-review
Open Access