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脂肪组织中脂类的过量积累会导致肥胖,进而引发心血管疾病、二型糖尿病和其他疾病。成脂是指干细胞分化为能够积聚脂滴的脂肪细胞的过程,受到一个复杂且高度协调的基因表达网络的调节,为促进成脂调控中关键基因和通路的发现,探索脂肪生成的分子调控机理,在之前的研究中,本实验室通过对成脂相关文献进行文本挖掘构建出一个包含3万多条成脂相关数据和信息的成脂调控网络(Adipogenesis Regulation Network,ARN)数据库(http://210.27.80.93/arn/)。为了进一步充分发掘ARN数据库促进成脂相关研究的潜在价值,本研究通过“开放的”和“闭合的”两种构建假说的原理,设计出能够用于分析成脂分化相关试验数据或构建科学假说的在线分析工具-ARN-analysis。另外,通过对成脂调控网络中各节点(基因和小分子RNA)的互作关系数、差异表达记录数和互作关系预测数进行统计分析,计算得到体现各节点重要性的节点影响值(impact factor,IF)。最后,通过对成脂调控网络中各节点的互作关系进行统计分析和作图,探索了成脂调控网络的拓扑结构。结果显示,ARN数据库的分析工具能够有效分析“节点相关”、“表达相关”及“用户录入”3类数据,帮助科研人员分析试验数据或构建科学假说。节点的IF值能够帮助科研人员快速识别重要的节点或假说,对调控网络拓扑结构的分析能加深对成脂调控机理的认识。本研究对成脂专业数据库的分析和预测功能的探索,为专业研究人员分析数据和构建假说提供了新的途径,探索了运用过去积累的大量科研数据促进未来的科研实践的可能性。
Excessive accumulation of lipids in adipose tissue can lead to obesity, leading to cardiovascular disease, type 2 diabetes and other diseases. Lipid differentiation refers to the process in which stem cells differentiate into adipocytes capable of accumulating lipid droplets and is regulated by a complex and highly coordinated gene expression network. In order to promote the discovery of key genes and pathways involved in adipogenic regulation and to explore the molecular regulation mechanism of adipogenesis In our previous study, we constructed an Adipogenesis Regulation Network (ARN) database containing more than 30,000 pieces of adipogenic related data and information by textual mining adipogenic related literature /210.27.80.93/arn/). In order to further explore the potential value of ARN database to promote adipogenesis-related research, this study designed the experimental data that can be used to analyze adipogenic differentiation through the principles of two “open” and “closed” construction hypotheses Or an online analytical tool to build scientific hypotheses - ARN-analysis. In addition, through the statistical analysis of the number of inter-relationships, the number of differentially expressed records and the predicted number of interactions of each node (genes and small RNAs) in the adipogenic regulatory network, the node influence values that reflect the importance of each node are calculated impact factor, IF). Finally, through the statistical analysis and mapping of the interaction between nodes in the adipogenic regulatory network, the topology of the adipogenic regulatory network is explored. The results show that the ARN database analysis tool can effectively analyze 3 kinds of data such as “node correlation”, “expression related” and “user input” to help researchers analyze experimental data or construct scientific hypothesis. The IF value of a node can help researchers quickly identify important nodes or hypotheses, and the analysis of a regulatory network topology can deepen the understanding of the mechanism of adipogenic regulation. The research on the analysis and prediction function of adipogenic professional database provides a new way for professional researchers to analyze data and construct hypothesis, and explores the possibility of using the large amount of scientific data accumulated in the past to promote future scientific research.