Clustering algorithms optimizer: a framework for large datasets
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Penalized independent component discriminant method for tumor classification
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
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Motivation: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. Results: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure. Availability: We have developed an R-package vabayelMix, available from www.cran.r-project.org, that implements the algorithm described in this paper. Contact: aet21@cam.ac.uk Supplementary information: http://bioinformatics.oxfordjournals.org