Work

Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods

Year: 2015

Type: article

Abstract: ) 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.

Source: EURASIP Journal on Bioinformatics and Systems Biology

DOI: 10.1186/s13637-015-0026-5

Authors: Jie Hou, Gary Stacey, Jianlin Cheng

Institution: University of Missouri

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Funders: National Science Foundation, U.S. Department of Energy, National Institutes of Health, National Institute of General Medical Sciences