Prediction of the mechanisms of salvia miltiorrhiza against atherosclerosis using text mining and network-based analysis

Abstract

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 atherosclerosis. The text mining results indicated atherosclerosis highly associated with salvia miltiorrhiza, and six genes associated with both. Protein-protein interaction information for these genes from databases and Literature data was searched and visualized using cytoscape. Four highly-connected regions were detected by IPCA algorithm to infer significant complexes or pathways in this network. The most relevant functions and pathways extracted from these subnetworks by BiNGO tool were related to I-kappaB kinase/NF-kappaB cascade, regulation of cellular process, regulation of biological process, which were consistent with previously published studies. Interestingly, insulin receptor signaling pathway was also implicated by network-based analysis in our study. Therefore, it was suggested that therapeutic mechanisms of salvia miltiorrhiza in atherosclerosis should be involved in increasing sensitivity and/or responsiveness to metabolic actions of insulin.