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Bioinformatics Vol. 18 no. 10 2002
Pages 1332-1339
© 2002 Oxford University Press

Bayesian automatic relevance determination algorithms for classifying gene expression data

Yi Li 1,, Colin Campbell 1,* and Michael Tipping 2

1 Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK
2 Microsoft Research, 7 J J Thomson Avenue, Cambridge, CB3 0FD, UK

Received on November 14, 2001 ; revised on April 22, 2002 ; accepted on April 26, 2002

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{at}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


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