Proceedings A marginalized variational bayesian approach to the analysis ofarray data

Machine Learning in Systems Biology: MLSB 2007

生物科学;医药卫生

Yiming Ying ; Peng Li ; Colin Campbell

Others  :  http://www.biomedcentral.com/content/pdf/1753-6561-2-S4-S7.pdf
PID  :  49543

来源: CEUR

【 摘 要 】

Background: Bayesian unsupervised learning methods havemany applications in the analysis of biological data. For example, for the cancer expression array datasets presented in this study, they can be used to resolve possible di sease subtypes and to indicate statistically significant dysregulated genes within these subtypes.Results:In this paper we outline a marginalizedvariational Bayesianinference method for unsupervised clustering. In this approach latent process variables and model parameters are allowed to be dependent.This is achieved by marginalizing the mixing Dirichlet variables and then performing inference in the reduced variable space. An iterative update procedure is proposed.Conclusion: Theoretically and experimentally we show that theproposed algorithm gives a much better free-energy lower bound than a standard variational Bayesian a pproach. The algorithm is computationally efficient and its performance is de monstrated on two expression array data sets.