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Interdisciplinary Bio Central v.2 no.2, 2010년, pp.2.1 - 2.4   피인용횟수: 1

StrokeMed: an integrated literature database for stroke and the differentiation of stroke syndrome

Kim, Young-Uk    (Korea Research Institute of Bioscience and Biotechnology   ); Kim, Jin-Ho    (Korea Research Institute of Bioscience and Biotechnology   ); Park, Young-Kyu    (Korea Research Institute of Bioscience and Biotechnology   ); Kim, Young-Joo    (Korea Research Institute of Bioscience and Biotechnology  );
  • 초록

    Complex diseases, such as stroke and cancer, have two or more genetic influences and are affected by environmental factors, which complicate them. Due to the complex characteristics of these diseases, we must search and study comprehensive literature-based article resources. Some disease-related literature databases have been developed through specialized journal issues or major websites. Most of them, however, are scattered throughout a website, and users encounter difficulties in finding accurate and comprehensive information easily and quickly. We developed StrokeMed, an integrated literature database for stroke and the differentiation of stroke syndrome. The system allows users to explore PubMed search results, categorized by MeSH (Medical Subject Headings), and the differentiation of stroke syndrome in Oriental medicine. StrokeMed collects data from important sites, such as PubMed, Scirus, and Scopus, automatically to maintain higher-quality and updated content. Currently, the system indexes more than 20,000 PubMed abstracts that are related to stroke, stroke etiology, and Oriental medicine. The system provides valuable literature information to the scientific and medical fields in stroke.


  • 주제어

    Stroke .   Stroke syndrome differentiation .   MeSH .   Text Mining .   Information extraction .   Information retrieval.  

  • 참고문헌 (13)

    1. Doms, A., and Schroeder, M. (2005). GoPubMed: exploring PubMed with the Gene Ontology. Nucleic Acids Res 7,783?786. 
    2. Alfred, E. (2006). HubMed: a web-based biomedical literature search interface. Nucleic Acids Res 34,745-747. 
    3. Hearst, M.A., Divoli, A., Guturu, H., Ksikes, A., Nakov, P., Wooldridge, M.A., and Ye, J. (2007). BioText Search Engine: beyond abstract search. Bioinformatics 23,2196-2197. 
    4. Hur, J., Schuyler, A.D., States, D.J., and Feldman, E.L. (2009). SciMiner: web-based literature mining tool for target identification and functional enrichment analysis. Bioinformatics 25,838-840. 
    5. Kim, Y.U., Kim, I.H., Bang, O.S., and Kim, Y.J. (2008). StrokeBase: A Database of Cerebrovascular Disease-related Candidate Gene. Genomics & informatics 6, 153-156.     
    6. Nakazato, T., Bono, H., Matsuda, H., and Takagi T. (2009). Gendoo: functional profiling of gene and disease features using MeSH vocabulary. Nucleic Acids Res 37,W166-W169. 
    7. Rhee, H., and Lee, J.S., (2007). PADB: published association database. BMC Bioinformatics 9, 348-355. 
    8. Tang, S., Zhang, Z., Kavitha, G., Tan, E.K., and Ng, S.K. (2009). MDPD: an integrated genetic information resource for Parkinson's disease. Nucleic Acids Res 1,858?862. 
    9. Yamamoto, Y., and Takagi, T. (2007). OReFiL: an online resource finder for life sciences. BMC Bioinformatics 8, 287-294. 
    10. Entrez Programming Utilities. http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/eutils_help.html 
    11. KoreaMed. http://koreamed.org/SearchBasic.php 
    12. MeSH. http://www.nlm.nih.gov/mesh/meshhome.html 
    13. PubMed. http://www.pubmed.gov/ 
  • 이 논문을 인용한 문헌 (1)

    1. 2010. "" Interdisciplinary Bio Central, 2(3): 7.1~7.3     

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