Multi-Dynamic Bayesian Networks for Pattern Recognition in Genomic DNA Sequences
Leo Wang-Kit Cheung1, Angel Yee-Man Cheung2
1lcheung@crch.hawaii.edu, Cancer Research lCenter of Hawaii, University of Hawaii ; 2angelymch@yahoo.com, Department of Computer Science, Chu Hai College
In parallel with the recent development of the novel Hidden Multivariate Markov
Models (HM3s) (Cheung, 2003), a family of Dynamic Bayesian Networks (DBNs)
is introduced for analyzing multi-dimensional genomic DNA data. This family of
Bayesian Networks, which we called Multi-Dynamic Bayesian Networks (MDBNs), is
designed with an overall network architecture that connects multiple DBNs. It
provides a flexible alternative
tool to
incorporate multiple sources of different kinds of data for recognition of
multiple patterns. We have been exploring the implementation of MDBNs with both
classical and empirical Bayesian ideas in order to enhance their applicability
to various areas of Bioinformatics and Computational Molecular Biology. The
focus of this poster is to show how a two-dimensional version of MDBNs can be
applied to recognition and prediction of eukaryotic
promoter regions. In particular, we make use of the discrete base-compositional
data and the continuous bendability (or structural bending propensity) data as
our two-dimensional DNA data in our applications. Illustrations
are shown via case studies using real human DNA data provided by Dr. Anders
Pedersen at the Center for Biological Sequence Analysis in
Denmark. Comparisons with the HM3s are also discussed.