Using text mining to understand traditional Chinese medicine pathogenesis of nonalcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a kind of prevalence diseases. Traditional Chinese medicine (TCM) has better efficacy on treating NAFLD. But there are also not known about the critical pathogenesis and the corresponding biological factors. Regarding this, we addressed a text mining approach to analyze the pattern profile, rule of medication, and the pathological factors of NAFLD from the opening database (SinoMed and PubMed). Based on canonical data source, we have our data treatment scheduled in 4 steps: (1) data retrieving, (2) data pretreating, (3) data analyzing, and (4) data visualization. And according to the TCM theory of formulae-pattern-disease´ correlation, we partly understand the possible TCM pathogenesis of NAFLD which linked biological process of lipid metabolism disorder, inflammation, and metabolic regulation confusion.