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| Analysis and Forecast Function of the Adipogenesis Regulation Network (ARN) Database |
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Abstract Excess accumulation of lipids in the adipose tissue leads to obesity, which is associated with cardiovascular diseases, type Ⅱ diabetes and other pathologies. Adipogenesis is the process by which precursor stem cells differentiate into lipid laden adipocytes. Adipogenesis is regulated by a complex and highly orchestrated gene expression program. In order to discovery key regulatory genes and pathways in adipogenesis and explore the molecular regulation mechanism of adipogenesis, in the previous study, we constructed a Adipogenesis Regulation Network (ARN) Database (http://210.27.80.93/arn/) by mining over 9 000 papers related to adipogenesis, which contains more than 30 000 adipogenesis related data and information. In order to fully explore the potential value of ARN database to promote the study of adipogenesis, in this study, we designed an online analysis tool "ARN-analysis" to analyze the experimental data or to construct the scientific hypothesis about adipogenesis through two hypothesis construction processes: "open" and "closed". Furthermore, by count the number of the relation records, the number of the expression records and the number of the prediction records for each node (gene and micro RNA), the impact factor (IF) value which reflects the importance of each node was calculated. Finally, by analyzing and mapping the number of the relation records of nodes, the topology of the adipogenesis regulation network was explored. The results showed that the analysis tool of ARN database can effectively analyze 3 kinds of data: "node" and "expression" and "user input", it would be useful for researchers to analyze test data or build scientific hypotheses. Understanding of the regulation network topology can deepen our understanding of the mechanism of the formation of fat. In this study the analysis and prediction functions of ARN database was explored, ARN provided a new way for professional researchers to analyze data and construct hypotheses, explored the possibility of using a large number of scientific research data accumulated in the past to promote future research and practice.
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Received: 28 April 2016
Published: 01 October 2016
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| Fund:;National 863 Program;National 863 Program |
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