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Bioinformatics Vol. 19 no. 9 2003
Pages 1061-1069
© 2003 Oxford University Press

Boosting for tumor classification with gene expression data

Marcel Dettling * and Peter Bühlmann

Seminar für Statistik, ETH Zürich, CH-8092, Switzerland

Received on February 28, 2002 ; revised on April 19, 2002 ; accepted on September 5, 2002

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

Contact: dettling{at}stat.math.ethz.ch

* To whom correspondence should be addressed.


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