alexa Bayesian automatic relevance determination algorithms for classifying gene expression data.
Genetics

Genetics

Advancements in Genetic Engineering

Author(s): Li Y, Campbell C, Tipping M

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Abstract 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.
This article was published in Bioinformatics and referenced in Advancements in Genetic Engineering

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