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

Hui Qin Zhang, Jian Li*, Guang Zheng, Miao Jiang, Li Li, Yong Tan, Aiping LYU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Pages311-314
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China
Duration: 18 Dec 201321 Dec 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

Conference

Conference2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Country/TerritoryChina
CityShanghai
Period18/12/1321/12/13

Scopus Subject Areas

  • Biomedical Engineering

User-Defined Keywords

  • Non-alcoholic liver disease
  • Pathogenesis
  • Text mining

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