Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation

Authors:
Tomonari Masada;Tsuyoshi Hamada;Yuichiro Shibata;Kiyoshi Oguri
Affiliations:
Nagasaki University, Nagasaki, Japan 852-8521;Nagasaki University, Nagasaki, Japan 852-8521;Nagasaki University, Nagasaki, Japan 852-8521;Nagasaki University, Nagasaki, Japan 852-8521
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

This paper provides a new method for multi-topic Bayesian analysis for microarray data. Our method achieves a further maximization of lower bounds in a marginalized variational Bayesian inference (MVB) for Latent Process Decomposition (LPD), which is an effective probabilistic model for microarray data. In our method, hyperparameters in LPD are updated by empirical Bayes point estimation. The experiments based on microarray data of realistically large size show efficiency of our hyperparameter reestimation technique.