Abstract
Populus is not only important for wood-based products, such as paper and timber, but also for metabolic-based production, for instance, bioethanol and biofuels. Constructing a sensibly functional gene interaction network is highly appealing to better understand system-level biological processes governing various Populus traits. Bayesian network learning provides an elegant and compact statistical approach for modeling causal gene-gene relationships in microarray data. Therefore, it could come with the illumination of functional molecular playing in Biology Systems. In this study, different forms of gene Bayesian networks were learned on Populus cellular transcriptome data. We addressed that Markov blankets, separating genes external to a regulatory Bayesian network from its internal genes, would likely be emerging at every possible gene regulatory Bayesian network level. The results have also shown that PtpAffx.1257.4.S1_a_at,1.0 hypothetical protein is the most important in its possible regulatory program. This paper illustrates that the gene network regulatory inference is possible to encapsulate within a single BN model. Therefore, such a BN model can serve as a promising training tool for Populus gene expression data to better prepare future experimental scenarios.
High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network
