BagBoosting for tumor classification with gene expression data.
BACKGROUND
Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which 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.


BACKGROUND
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.
Seminar für Statistik, ETH Zürich, CH-8092 Switzerland. dettling@stat.math.ethz.ch <dettling@stat.math.ethz.ch>
Marcel
M
Dettling
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics*; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins