title: Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods.
abstract: Soybean (Glycine max) is a major source of vegetable oil and protein for both animal and human consumption. The completion of soybean genome sequence led to a number of transcriptomic studies (RNA-seq), which provide a resource for gene discovery and functional analysis. Several data-driven (e.g., based on gene expression data) and knowledge-based (e.g., predictions of molecular interactions) methods have been proposed and implemented. In order to better understand gene relationships and protein interactions, we applied probabilistic graphical methods, based on Bayesian network and knowledgebase constraints using gene expression data to reconstruct soybean metabolic pathways. The results show that this method can predict new relationships between genes, improving on traditional reference pathway maps.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211 USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Hou
authors.affiliation: Divisions of Biochemistry and Plant Science, National Center for Soybean Biotechnology, C. Bond Life Science Center, University of Missouri, Columbia, MO 65211 USA.
authors.forename: Gary
authors.initials: G
authors.lastname: Stacey
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211 USA.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
keywords: Bayesian network; Gene expression data; KEGG database; Metabolic pathway; RNA-seq; Soybean