Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data


Ka Yee Yeung, Roger E. Bumgarner, and Adrian E. Raftery


Abstract
Motivation: Selecting a small number of relevant genes for accurate classification of samples is essential for 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 showed that BMA selects smaller numbers of relevant genes (compared to other methods) and achieves 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.