Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients
Classification of patients based on molecular markers, for example into different risk groups, is a modern field in medical research. The aim of this classification is often a better diagnosis or individualized therapy. The search for molecular markers often utilizes extremely high-dimensional data...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
17 February 2011
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| In: |
Biometrical journal
Year: 2011, Jahrgang: 53, Heft: 2, Pages: 190-201 |
| ISSN: | 1521-4036 |
| DOI: | 10.1002/bimj.201000155 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/bimj.201000155 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201000155 
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| Verfasserangaben: | Christine Porzelius, Marc Johannes, Harald Binder, and Tim Beißbarth |
MARC
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| 520 | |a Classification of patients based on molecular markers, for example into different risk groups, is a modern field in medical research. The aim of this classification is often a better diagnosis or individualized therapy. The search for molecular markers often utilizes extremely high-dimensional data sets (e.g. gene-expression microarrays). However, in situations where the number of measured markers (genes) is intrinsically higher than the number of available patients, standard methods from statistical learning fail to deal correctly with this so-called “curse of dimensionality”. Also feature or dimension reduction techniques based on statistical models promise only limited success. Several recent methods explore ideas of how to quantify and incorporate biological prior knowledge of molecular interactions and known cellular processes into the feature selection process. This article aims to give an overview of such current methods as well as the databases, where this external knowledge can be obtained from. For illustration, two recent methods are compared in detail, a feature selection approach for support vector machines as well as a boosting approach for regression models. As a practical example, data on patients with acute lymphoblastic leukemia are considered, where the binary endpoint “relapse within first year” should be predicted. | ||
| 650 | 4 | |a Classification | |
| 650 | 4 | |a Gene expression data | |
| 650 | 4 | |a Pathway knowledge | |
| 650 | 4 | |a Risk prediction | |
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