Mark A. van de Wiel

10.5k total citations · 2 hit papers
165 papers, 7.3k citations indexed

About

Mark A. van de Wiel is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Mark A. van de Wiel has authored 165 papers receiving a total of 7.3k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Molecular Biology, 38 papers in Cancer Research and 33 papers in Genetics. Recurrent topics in Mark A. van de Wiel's work include Gene expression and cancer classification (41 papers), Genomic variations and chromosomal abnormalities (21 papers) and Statistical Methods and Inference (20 papers). Mark A. van de Wiel is often cited by papers focused on Gene expression and cancer classification (41 papers), Genomic variations and chromosomal abnormalities (21 papers) and Statistical Methods and Inference (20 papers). Mark A. van de Wiel collaborates with scholars based in Netherlands, United States and United Kingdom. Mark A. van de Wiel's co-authors include Torsten Hothorn, Kurt Hornik, Achim Zeileis, Wessel N. van Wieringen, Bauke Ylstra, Gerrit A. Meijer, Saskia M. Wilting, Beatriz Carvalho, Ruud H. Brakenhoff and Peter J.F. Snijders and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Mark A. van de Wiel

159 papers receiving 7.1k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Implementing a Class of Permutation Tests: ThecoinPackage

2008 1.0k citations Mark A. van de Wiel et al. profile →
A Lego System for Conditional Inference

2006 628 citations Mark A. van de Wiel et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mark A. van de Wiel Netherlands	41	2.9k	1.8k	1.1k	971	762	165	7.3k
Arnold J. Stromberg United States	51	3.3k 1.1×	1.3k 0.7×	1.4k 1.3×	648 0.7×	284 0.4×	256	9.1k
Jelle J. Goeman Netherlands	43	3.5k 1.2×	785 0.4×	516 0.5×	931 1.0×	294 0.4×	180	9.8k
Alexander Ploner Sweden	39	2.4k 0.8×	1.1k 0.6×	1.4k 1.2×	647 0.7×	214 0.3×	124	6.3k
Natacha Turck Switzerland	31	3.5k 1.2×	1.1k 0.6×	884 0.8×	652 0.7×	428 0.6×	53	11.1k
Alexandre Hainard Switzerland	19	3.1k 1.1×	1.1k 0.6×	813 0.7×	595 0.6×	350 0.5×	31	10.1k
Malcolm A. Smith United States	55	4.9k 1.7×	1.6k 0.9×	3.3k 3.0×	1.0k 1.1×	975 1.3×	292	12.7k
Xavier Robin Switzerland	21	3.0k 1.0×	1.1k 0.6×	805 0.7×	575 0.6×	335 0.4×	35	9.8k
Annette M. Molinaro United States	48	2.7k 0.9×	1.7k 0.9×	1.5k 1.3×	340 0.4×	483 0.6×	222	9.3k
Jean-Charles Sanchez Switzerland	8	2.5k 0.9×	1.0k 0.6×	748 0.7×	556 0.6×	323 0.4×	14	9.0k
Natalia Tiberti Italy	17	2.6k 0.9×	1.0k 0.6×	762 0.7×	564 0.6×	341 0.4×	45	9.5k

Countries citing papers authored by Mark A. van de Wiel

Since Specialization

Citations

This map shows the geographic impact of Mark A. van de Wiel's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark A. van de Wiel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark A. van de Wiel more than expected).

Fields of papers citing papers by Mark A. van de Wiel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark A. van de Wiel. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark A. van de Wiel. The network helps show where Mark A. van de Wiel may publish in the future.

Co-authorship network of co-authors of Mark A. van de Wiel

This figure shows the co-authorship network connecting the top 25 collaborators of Mark A. van de Wiel. A scholar is included among the top collaborators of Mark A. van de Wiel based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mark A. van de Wiel. Mark A. van de Wiel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Borodzicz-Jażdżyk, Sonia, et al.. (2025). Stress T1 mapping and quantitative perfusion cardiovascular magnetic resonance in patients with suspected obstructive coronary artery disease. European Heart Journal - Cardiovascular Imaging. 26(6). 980–990.

Wiel, Mark A. van de, et al.. (2024). Linked shrinkage to improve estimation of interaction effects in regression models. PubMed. 13(1). 20230039–20230039. 2 indexed citations

Nauta, Irene H., K Grünewald, Arjen Brink, et al.. (2024). Hallmarks of a genomically distinct subclass of head and neck cancer. Nature Communications. 15(1). 9060–9060. 7 indexed citations

Wiel, Mark A. van de, et al.. (2021). Fast Cross-validation for Multi-penalty High-dimensional Ridge Regression. Journal of Computational and Graphical Statistics. 30(4). 835–847. 16 indexed citations

Mourragui, Soufiane, Marco Loog, Daniël J. Vis, et al.. (2021). Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning. Proceedings of the National Academy of Sciences. 118(49). 24 indexed citations

Wieringen, Wessel N. van, et al.. (2020). Updating of the Gaussian graphical model through targeted penalized estimation. Journal of Multivariate Analysis. 178. 104621–104621. 4 indexed citations

Roest, Reinout H. de, Steven W. Mes, Jos B. Poell, et al.. (2019). Molecular Characterization of Locally Relapsed Head and Neck Cancer after Concomitant Chemoradiotherapy. Clinical Cancer Research. 25(23). 7256–7265. 17 indexed citations

Mourragui, Soufiane, Marco Loog, Mark A. van de Wiel, Marcel Reinders, & Lodewyk F.A. Wessels. (2019). PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinformatics. 35(14). i510–i519. 45 indexed citations

Verlaat, Wina, Barbara C. Snoek, Daniëlle A.M. Heideman, et al.. (2018). Identification and Validation of a 3-Gene Methylation Classifier for HPV-Based Cervical Screening on Self-Samples. Clinical Cancer Research. 24(14). 3456–3464. 57 indexed citations

10.

Vries, Teun J. de, Ton Schoenmaker, Lilyanne C. Grevers, et al.. (2014). M‐CSF Priming of Osteoclast Precursors Can Cause Osteoclastogenesis‐Insensitivity, Which Can be Prevented and Overcome on Bone. Journal of Cellular Physiology. 230(1). 210–225. 40 indexed citations

11.

Wilting, Saskia M., Mark A. van de Wiel, Annelieke Jaspers, et al.. (2014). tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities. BMC Bioinformatics. 15(1). 327–327. 2 indexed citations

12.

Voorham, Quirinus J.M., Beatriz Carvalho, Nicole C.T. van Grieken, et al.. (2012). Chromosome 5q Loss in Colorectal Flat Adenomas. Clinical Cancer Research. 18(17). 4560–4569. 26 indexed citations

13.

Sutedja, Thomas G., Peter J.F. Snijders, Saskia M. Wilting, et al.. (2011). DNA Copy Number Alterations in Endobronchial Squamous Metaplastic Lesions Predict Lung Cancer. American Journal of Respiratory and Critical Care Medicine. 184(8). 948–956. 26 indexed citations

14.

Wieringen, Wessel N. van, Johannes Berkhof, & Mark A. van de Wiel. (2010). A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number. Statistical Applications in Genetics and Molecular Biology. 9(1). Article25–Article25. 6 indexed citations

15.

Roquain, Étienne, et al.. (2010). Spatial Clustering of Array CGH Features in Combination with Hierarchical Multiple Testing. Statistical Applications in Genetics and Molecular Biology. 9(1). Article40–Article40. 3 indexed citations

16.

Röser, Kerstin, Thomas Löning, Bauke Ylstra, et al.. (2008). Copy number gain at 8q12.1‐q22.1 is associated with a malignant tumor phenotype in salivary gland myoepitheliomas. Genes Chromosomes and Cancer. 48(2). 202–212. 13 indexed citations

17.

Pham, Thang V., Mark A. van de Wiel, & Connie R. Jiménez. (2008). Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples. Statistical Applications in Genetics and Molecular Biology. 7(2). Article11–Article11. 7 indexed citations

18.

Chin, Suet‐Feung, Andrew E. Teschendorff, John C. Marioni, et al.. (2007). High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer. Genome biology. 8(10). R215–R215. 239 indexed citations

19.

Wiel, Mark A. van de, José Luís Costa, Kees Smid, et al.. (2005). Expression Microarray Analysis and Oligo Array Comparative Genomic Hybridization of Acquired Gemcitabine Resistance in Mouse Colon Reveals Selection for Chromosomal Aberrations. Cancer Research. 65(22). 10208–10213. 24 indexed citations

20.

Wiel, Mark A. van de. (1998). Exact distributions of two-sample rank statistics and block rank statistics using computer algebra. 9814. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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