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Bioinformatics Advance Access published online on October 5, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth447
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received June 5, 2004
Revised July 5, 2004
Accepted July 9, 2004

Article

BagBoosting for tumor classification with gene expression data

Marcel Dettling 1* and E. T. H. Zürich 2

1 Seminar für Statistik
2 CH-8092 Switzerland

* To whom correspondence should be addressed. E-mail: dettling{at}stat.math.ethz.ch.


  Abstract

Motivation: Microarray experiments are expected to contribute significantly to progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools that can deal with a large number of highly correlated input variables, perform feature selection, and provide class probability estimates that serve as a quantification of the predictive uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting to a novel algorithm called BagBoosting.

Results: When bagging is used as a module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also confirmed by comparing BagBoosting to several established class prediction tools for microarray data.

Availability: Software for the modified boosting algorithms, for benchmark studies and for the simulation of microarray data are available as an R package under GNU public license at http://stat.ethz.ch/~dettling/bagboost.html.


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