Prediction of therapeutic mechanisms of tripterygium wilfordii in rheumatoid arthritis using text 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.