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pro vyhledávání: '"van Nee, Mirrelijn"'
High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data, providing compl
Externí odkaz:
http://arxiv.org/abs/2205.07640
Nowadays, clinical research routinely uses omics data, such as gene expression, for predicting clinical outcomes or selecting markers. Additionally, so-called co-data are often available, providing complementary information on the covariates, like p-
Externí odkaz:
http://arxiv.org/abs/2101.03875
High-dimensional prediction with multiple data types needs to account for potentially strong differences in predictive signal. Ridge regression is a simple model for high-dimensional data that has challenged the predictive performance of many more co
Externí odkaz:
http://arxiv.org/abs/2005.09301
Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dimensional, but additional information, like domain knowledge and previou
Externí odkaz:
http://arxiv.org/abs/2005.04010
Autor:
van Nee, Mirrelijn M.1 (AUTHOR) m.vannee@amsterdamumc.nl, Wessels, Lodewyk F. A.2,3,4 (AUTHOR), van de Wiel, Mark A.1 (AUTHOR)
Publikováno v:
BMC Bioinformatics. 4/26/2023, Vol. 24 Issue 1, p1-16. 16p.
Akademický článek
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Publikováno v:
In Ocean Engineering 15 November 2017 145:359-372
Autor:
van Nee, Mirrelijn Meander
Publikováno v:
van Nee, M M 2023, ' Co-data learning : Statistical methods to inform high-dimensional clinical prediction with complementary data ', PhD, Vrije Universiteit Amsterdam, s.l. . https://doi.org/10.5463/thesis.191
van Nee, M M 2023, ' Co-data learning : Statistical methods to inform high-dimensional clinical prediction with complementary data ', Doctor of Philosophy, Vrije Universiteit Amsterdam, s.l. . < https://research.vu.nl/en/publications/co-data-learning-statistical-methods-to-inform-high-dimensional-c >
van Nee, M M 2023, ' Co-data learning : Statistical methods to inform high-dimensional clinical prediction with complementary data ', Doctor of Philosophy, Vrije Universiteit Amsterdam, s.l. . < https://research.vu.nl/en/publications/co-data-learning-statistical-methods-to-inform-high-dimensional-c >
Clinicians often research complex traits in which many variables may be involved in the process underlying the disease. Nowadays, with the advent of DNA sequencing techniques, clinical studies regularly research high‐dimensional data, in which the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::35f30874486f308a9963f1b956228f84
https://doi.org/10.5463/thesis.191
https://doi.org/10.5463/thesis.191
Autor:
Mourragui, S.M.C., Loog, M., van Nee, Mirrelijn, de Wiel, Mark A.van, Reinders, M.J.T., Wessels, L.F.A., Tang, Haixu
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031291180
Research in Computational Molecular Biology-27th Annual International Conference, RECOMB 2023, Proceedings
Research in Computational Molecular Biology-27th Annual International Conference, RECOMB 2023, Proceedings
Motivation: Anti-cancer drugs may elicit resistance or sensitivity through mechanisms which involve several genomic layers. Nevertheless, we have demonstrated that gene expression contains most of the predictive capacity compared to the remaining omi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::183fa41d60e47fc5fe4ec6222c54cc77
https://doi.org/10.1007/978-3-031-29119-7_8
https://doi.org/10.1007/978-3-031-29119-7_8
Publikováno v:
Journal of Computational & Graphical Statistics. Jul-Sep2023, Vol. 32 Issue 3, p950-960. 11p.