• DocumentCode
    2590844
  • Title

    Identifying gene-disease associations using word proximity and similarity of Gene Ontology terms

  • Author

    Hou, Wen-Juan ; Chen, Li-Che ; Lu, Chieh-Shiang

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan
  • Volume
    4
  • fYear
    2011
  • fDate
    15-17 Oct. 2011
  • Firstpage
    1748
  • Lastpage
    1752
  • Abstract
    Associating genes with diseases is an active area of research because it is useful for helping human health with applications to clinical diagnosis and therapy. This paper proposes two methods to guide the associations between genes and diseases: (1) making use of the proximity relationship between genes and diseases and (2) utilizing GO terms shared by genes and diseases for similarity comparison. The experiments show that associations utilizing GO terms perform better than using word proximity. The results reveal that the GO terms act as a good gene-disease association feature.
  • Keywords
    cellular biophysics; diseases; genetics; medical diagnostic computing; ontologies (artificial intelligence); patient diagnosis; patient treatment; clinical diagnosis; gene ontology terms; gene-disease associations; human health; similarity; therapy; word proximity; Bioinformatics; Databases; Diseases; Humans; Ontologies; Proteins; Tagging; Gene Ontology; bioinformatics; gene-disease association; text mining; word proximity relationship;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-9351-7
  • Type

    conf

  • DOI
    10.1109/BMEI.2011.6098702
  • Filename
    6098702