• DocumentCode
    2519423
  • Title

    Seeking Potential Biological Network Shared in Rheumatoid Arthritis and Ulcerative Colitis through Text Mining

  • Author

    Ding, Xiaorong ; Zha, Qinglin ; Lu, Aiping

  • Author_Institution
    Inst. of Basic Res. In Clinical Med., China Acad. of Chinese Med. Sci., Beijing, China
  • fYear
    2009
  • fDate
    11-13 June 2009
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    In traditional Chinese medicine, different diseases might be treated with the same therapy if they show the same pattern, which suggests that these different diseases could have the similar biological pathway or network in some specific fields. Rheumatoid arthritis (RA) and ulcerative colitis (UC) were taken for the potential shared biological network exploration with data mining approach. Text mining tool was applied to analyze the PubMed literature database and integrate the available biological information to seeking the potential biological network shared in RA and UC. The results show that RA and UC have immune and metabolically shared network obtained from analysis on the PubMed database with text mining. It could be concluded that the text mining could be used to explore the potentially biological network shared by different diseases, which further help for leading some new findings in human or animal study.
  • Keywords
    data mining; database management systems; diseases; medical information systems; orthopaedics; patient treatment; PubMed database; biological network sharing; biological pathway; data mining; disease therapy; diseases; rheumatoid arthritis; text mining; traditional Chinese medicine; ulcerative colitis; Animals; Arthritis; Data mining; Databases; Diseases; Humans; Immune system; Information analysis; Medical treatment; Text mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-2901-1
  • Electronic_ISBN
    978-1-4244-2902-8
  • Type

    conf

  • DOI
    10.1109/ICBBE.2009.5163368
  • Filename
    5163368