van de Wiel MA - Search Results

1

Support vector machine approach to separate control and breast cancer serum samples.

Pham TV, van de Wiel MA, Jimenez CR. Pham TV, et al. Among authors: van de wiel ma. Stat Appl Genet Mol Biol. 2008;7(2):Article11. doi: 10.2202/1544-6115.1355. Epub 2008 Feb 21. Stat Appl Genet Mol Biol. 2008. PMID: 18312216

2

Proteins in stool as biomarkers for non-invasive detection of colorectal adenomas with high risk of progression.

Komor MA, Bosch LJ, Coupé VM, Rausch C, Pham TV, Piersma SR, Mongera S, Mulder CJ, Dekker E, Kuipers EJ, van de Wiel MA, Carvalho B, Fijneman RJ, Jimenez CR, Meijer GA, de Wit M. Komor MA, et al. Among authors: de wit m, van de wiel ma. J Pathol. 2020 Mar;250(3):288-298. doi: 10.1002/path.5369. Epub 2020 Jan 13. J Pathol. 2020. PMID: 31784980 Free PMC article.

3

Microarray data analysis: from hypotheses to conclusions using gene expression data.

Armstrong NJ, van de Wiel MA. Armstrong NJ, et al. Among authors: van de wiel ma. Cell Oncol. 2004;26(5-6):279-90. doi: 10.1155/2004/943940. Cell Oncol. 2004. PMID: 15623938 Free PMC article. Review.

4

Stepwise classification of cancer samples using clinical and molecular data.

Obulkasim A, Meijer GA, van de Wiel MA. Obulkasim A, et al. Among authors: van de wiel ma. BMC Bioinformatics. 2011 Oct 28;12:422. doi: 10.1186/1471-2105-12-422. BMC Bioinformatics. 2011. PMID: 22034839 Free PMC article.

5

Estimating the false discovery rate using nonparametric deconvolution.

van de Wiel MA, Kim KI. van de Wiel MA, et al. Biometrics. 2007 Sep;63(3):806-15. doi: 10.1111/j.1541-0420.2006.00736.x. Biometrics. 2007. PMID: 17825012

6

A random coefficients model for regional co-expression associated with DNA copy number.

van Wieringen WN, Berkhof J, van de Wiel MA. van Wieringen WN, et al. Among authors: van de wiel ma. Stat Appl Genet Mol Biol. 2010;9:Article25. doi: 10.2202/1544-6115.1531. Epub 2010 Jun 22. Stat Appl Genet Mol Biol. 2010. PMID: 20597851

7

Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.

Van De Wiel MA, Leday GG, Pardo L, Rue H, Van Der Vaart AW, Van Wieringen WN. Van De Wiel MA, et al. Among authors: van wieringen wn, van der vaart aw. Biostatistics. 2013 Jan;14(1):113-28. doi: 10.1093/biostatistics/kxs031. Epub 2012 Sep 17. Biostatistics. 2013. PMID: 22988280

8

General power and sample size calculations for high-dimensional genomic data.

van Iterson M, van de Wiel MA, Boer JM, de Menezes RX. van Iterson M, et al. Among authors: de menezes rx, van de wiel ma. Stat Appl Genet Mol Biol. 2013 Aug;12(4):449-67. doi: 10.1515/sagmb-2012-0046. Stat Appl Genet Mol Biol. 2013. PMID: 23934609

9

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

Slieker RC, Münch M, Donnelly LA, Bouland GA, Dragan I, Kuznetsov D, Elders PJM, Rutter GA, Ibberson M, Pearson ER, 't Hart LM, van de Wiel MA, Beulens JWJ. Slieker RC, et al. Among authors: van de wiel ma. Diabetologia. 2024 May;67(5):885-894. doi: 10.1007/s00125-024-06105-8. Epub 2024 Feb 19. Diabetologia. 2024. PMID: 38374450 Free PMC article.

10

Weighted clustering of called array CGH data.

Van Wieringen WN, Van De Wiel MA, Ylstra B. Van Wieringen WN, et al. Among authors: van de wiel ma. Biostatistics. 2008 Jul;9(3):484-500. doi: 10.1093/biostatistics/kxm048. Epub 2007 Dec 22. Biostatistics. 2008. PMID: 18156651

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