Title |
Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods
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Published in |
EURASIP Journal on Bioinformatics & Systems Biology, June 2015
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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
Geographical breakdown
Country | Count | As % |
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Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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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 % |
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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% |