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25 results on '"Mes, Steven W."'

2. Stable prediction with radiomics data

Author

Peeters, Carel F. W., Übelhör, Caroline, Mes, Steven W., Martens, Roland, Koopman, Thomas, de Graaf, Pim, van Velden, Floris H. P., Boellaard, Ronald, Castelijns, Jonas A., Beest, Dennis E. te, Heymans, Martijn W., and van de Wiel, Mark A.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods, Statistics - Applications, Statistics - Methodology
Abstract

Motivation: Radiomics refers to the high-throughput mining of quantitative features from radiographic images. It is a promising field in that it may provide a non-invasive solution for screening and classification. Standard machine learning classification and feature selection techniques, however, tend to display inferior performance in terms of (the stability of) predictive performance. This is due to the heavy multicollinearity present in radiomic data. We set out to provide an easy-to-use approach that deals with this problem. Results: We developed a four-step approach that projects the original high-dimensional feature space onto a lower-dimensional latent-feature space, while retaining most of the covariation in the data. It consists of (i) penalized maximum likelihood estimation of a redundancy filtered correlation matrix. The resulting matrix (ii) is the input for a maximum likelihood factor analysis procedure. This two-stage maximum-likelihood approach can be used to (iii) produce a compact set of stable features that (iv) can be directly used in any (regression-based) classifier or predictor. It outperforms other classification (and feature selection) techniques in both external and internal validation settings regarding survival in squamous cell cancers., Comment: 52 pages: 14 pages Main Text and 38 pages of Supplementary Material

Published
2019

4. Improved high-dimensional prediction with Random Forests by the use of co-data

Author

Beest, Dennis E. te, Mes, Steven W., Brakenhoff, Ruud H., and van de Wiel, Mark A.

Subjects
Statistics - Applications
Abstract

Prediction in high dimensional settings is difficult due to large by number of variables relative to the sample size. We demonstrate how auxiliary "co-data" can be used to improve the performance of a Random Forest in such a setting. Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities (used to draw candidate variables, the default for a Random Forest) by co-data moderated sampling probabilities. Co-data here is defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate this co-data moderated Random Forest (CoRF) with one example. In the example we aim to predict a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance., Comment: 17 pages, 4 figures, to be published

Published
2017

15. Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures

Author

Onderzoek Radiotherapie, Cancer, MS Hoofd-Hals Chirurgische Oncologie, MS Radiologie, Mes, Steven W, van Velden, Floris H P, Peltenburg, Boris, Peeters, Carel F W, Te Beest, Dennis E, van de Wiel, Mark A, Mekke, Joost, Mulder, Doriene C, Martens, Roland M, Castelijns, Jonas A, Pameijer, Frank A, de Bree, Remco, Boellaard, Ronald, Leemans, C René, Brakenhoff, Ruud H, de Graaf, Pim, Onderzoek Radiotherapie, Cancer, MS Hoofd-Hals Chirurgische Oncologie, MS Radiologie, Mes, Steven W, van Velden, Floris H P, Peltenburg, Boris, Peeters, Carel F W, Te Beest, Dennis E, van de Wiel, Mark A, Mekke, Joost, Mulder, Doriene C, Martens, Roland M, Castelijns, Jonas A, Pameijer, Frank A, de Bree, Remco, Boellaard, Ronald, Leemans, C René, Brakenhoff, Ruud H, and de Graaf, Pim

Published
2020

20. Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables

Author

Mes, Steven W., primary, te Beest, Dennis, additional, Poli, Tito, additional, Rossi, Silvia, additional, Scheckenbach, Kathrin, additional, van Wieringen, Wessel N., additional, Brink, Arjen, additional, Bertani, Nicoletta, additional, Lanfranco, Davide, additional, Silini, Enrico M., additional, van Diest, Paul J., additional, Bloemena, Elisabeth, additional, Leemans, C. René, additional, van de Wiel, Mark A., additional, and Brakenhoff, Ruud H., additional

Published
2017
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21. Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables

Author

Mes, Steven W., te Beest, Dennis, Poli, Tito, Rossi, Silvia, Scheckenbach, Kathrin, Van Wieringen, Wessel N., Brink, Arjen, Bertani, Nicoletta, Lanfranco, Davide, Silini, Enrico M., van Diest, Paul J., Bloemena, Elisabeth, René Leemans, C., Van De Wiel, Mark A., Brakenhoff, Ruud H, Mes, Steven W., te Beest, Dennis, Poli, Tito, Rossi, Silvia, Scheckenbach, Kathrin, Van Wieringen, Wessel N., Brink, Arjen, Bertani, Nicoletta, Lanfranco, Davide, Silini, Enrico M., van Diest, Paul J., Bloemena, Elisabeth, René Leemans, C., Van De Wiel, Mark A., and Brakenhoff, Ruud H

Published
2017

22. Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables

Author

Pathologie mortuarium en obductie, UMC Utrecht, Pathologie, Cancer, Mes, Steven W., te Beest, Dennis, Poli, Tito, Rossi, Silvia, Scheckenbach, Kathrin, Van Wieringen, Wessel N., Brink, Arjen, Bertani, Nicoletta, Lanfranco, Davide, Silini, Enrico M., van Diest, Paul J., Bloemena, Elisabeth, René Leemans, C., Van De Wiel, Mark A., Brakenhoff, Ruud H, Pathologie mortuarium en obductie, UMC Utrecht, Pathologie, Cancer, Mes, Steven W., te Beest, Dennis, Poli, Tito, Rossi, Silvia, Scheckenbach, Kathrin, Van Wieringen, Wessel N., Brink, Arjen, Bertani, Nicoletta, Lanfranco, Davide, Silini, Enrico M., van Diest, Paul J., Bloemena, Elisabeth, René Leemans, C., Van De Wiel, Mark A., and Brakenhoff, Ruud H

Published
2017

25. MS-associated gene CLEC16A uses the molecular machinery of late endosomal biogenesis to control HLA-II expression in APC

Author

Van Luijn, Marvin M., primary, Kreft, Karim L., additional, Jongsma, Marlieke L., additional, Mes, Steven W., additional, Wierenga-wolf, Annet F., additional, Van Meurs, Marjan, additional, Melief, Marie-josé, additional, Van Der Kant, Rik, additional, Janssen, Lennert, additional, Janssen, Hans, additional, Priatel, John J., additional, Tan, Rusung, additional, Neefjes, Jacques, additional, Laman, Jon D., additional, and Hintzen, Rogier Q., additional

Published
2014
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