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

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, June 2015
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Title
Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods
Published in
EURASIP Journal on Bioinformatics & Systems Biology, June 2015
DOI 10.1186/s13637-015-0026-5
Pubmed ID
Authors

Jie Hou, Gary Stacey, Jianlin Cheng

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 21%
Researcher 4 17%
Student > Doctoral Student 3 13%
Student > Bachelor 3 13%
Student > Ph. D. Student 2 8%
Other 4 17%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 21%
Computer Science 4 17%
Engineering 3 13%
Biochemistry, Genetics and Molecular Biology 2 8%
Business, Management and Accounting 1 4%
Other 4 17%
Unknown 5 21%