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Boosting for tumor classification with gene expression data
<span title="2003-06-12">2003</span>
<i title="Oxford University Press (OUP)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a>
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Motivation: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees. Results: We demonstrate that the generic boosting algorithm needs some modification to become an
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btf867">doi:10.1093/bioinformatics/btf867</a>
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... ccurate classifier in the context of gene expression data. In particular, we present a feature preselection method, a more robust boosting procedure and a new approach for multicategorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets.
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