Prediction of Therapeutic Mechanisms of Tripterygium Wilfordii in Rheumatoid Arthritis Using Tezt Mining and Network-based Analysis
We combine text mining with methods of systems biology for the first time, to predict functional networks for therapeutic mechanisms of Traditional Chinese Medicine in rheumatoid arthritis. The text mining results indicated rheumatoid arthritis highly associated with Tripterygium wilfordii, and eleven genes associated with both. Protein interaction information for these genes from databases and Literature data was visualized using cytoscape. Five highly-connected regions were detected by IPCA algorithm in this network. The most relevant functions and pathways were extracted from these subnetworks by BiNGO tool. Interestingly, regulation of defense response to virus and viral reproductive process were implicated by network-based analysis. Therefore, it was suggested that therapeutic mechanisms of Tripterygium wilfordii in rheumatoid arthritis should be involved in suppressing viral protein synthesis of infected cells and antiviral immune responses.
rheumatoid arthritis tezt mining systems biology tripterygium wilfordii antivirus
CHEN Gao JIANG Miao LV Cheng LU Ai-ping
School of Life Sciences, Hubei University, Wuhan, Hubei, 430062, P.R.China Institute of Basic Resear Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Science,Beijing, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Science, Beijing,
国际会议
2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)
济南
英文
115-119
2009-08-14（万方平台首次上网日期，不代表论文的发表时间）