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Bioinformatics. 2003 Jun 12;19(9):1061-9.

Boosting for tumor classification with gene expression data.

Author information

  • 1Seminar für Statistik, ETH Zürich, CH-8092, Switzerland. dettling@stat.math.ethz.ch

Abstract

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 accurate 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 multi-categorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets.

AVAILABILITY:

Software for the modified boosting algorithms as well as for decision trees is available for free in R at http://stat.ethz.ch/~dettling/boosting.html.

PMID:
12801866
[PubMed - indexed for MEDLINE]
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