Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data.
BACKGROUND
Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests. We present the Bayesian model averaging (BMA) method for gene selection and classification of microarray data. Typical gene selection and classification procedures ignore model uncertainty and use a single set of relevant genes (model) to predict the class. BMA accounts for the uncertainty about the best set to choose by averaging over multiple models (sets of potentially overlapping relevant genes).


RESULTS
We have shown that BMA selects smaller numbers of relevant genes (compared with other methods) and achieves a high prediction accuracy on three microarray datasets. Our BMA algorithm is applicable to microarray datasets with any number of classes, and outputs posterior probabilities for the selected genes and models. Our selected models typically consist of only a few genes. The combination of high accuracy, small numbers of genes and posterior probabilities for the predictions should make BMA a powerful tool for developing diagnostics from expression data.


BACKGROUND
The source codes and datasets used are available from our Supplementary website.
Yeung
Ka Yee
KY
Department of Microbiology, University of Washington, Seattle, WA 98195, USA. kayee@u.washington.edu
Bumgarner
Roger E
RE
Raftery
Adrian E
AE
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000032:analysis* / Q000235:genetics; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins