Matches in UGent Biblio for { <https://biblio.ugent.be/publication/878859#aggregation> ?p ?o. }
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- aggregation classification "A1".
- aggregation creator B413006.
- aggregation creator B413007.
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- aggregation creator person.
- aggregation creator person.
- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 878859.bibtex.
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- aggregation isPartOf urn:issn:1367-4803.
- aggregation language "eng".
- aggregation publisher "Oxford University Press".
- aggregation rights "I don't know the status of the copyright for this publication".
- aggregation subject "Science General".
- aggregation title "Robust biomarker identification for cancer diagnosis with ensemble feature selection methods".
- aggregation abstract "Motivation: Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method. Results: Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the- art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of similar to 15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature.".
- aggregation authorList BK731762.
- aggregation endPage "398".
- aggregation issue "3".
- aggregation startPage "392".
- aggregation volume "26".
- aggregation aggregates 2989432.
- aggregation isDescribedBy 878859.
- aggregation similarTo btp630.
- aggregation similarTo LU-878859.