Bayesian automatic relevance determination algorithms for classifying gene expression data

Motivation: We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays. Results: We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatically incorporating feature selection, accurate classifiers can be constructed utilizing very few features and with minimal hand-tuning. We argue that the feature selection is meaningful and some of the highlighted genes appear to be medically important. Contact: C.Campbell@bris.ac.uk * To whom correspondence should be addressed. Present address: Information and Mathematical Sciences, Genome Institute of Singapore, 1 Science Park Road, The Capricorn #05-01, Singapore 117528, Republic of Singapore