• DocumentCode
    2781766
  • Title

    Mining association rules from HIV-human protein interactions

  • Author

    Mukhopadhyay, Anirban ; Maulik, Ujjwal ; Bandyopadhyay, Sanghamitra ; Eils, Roland

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of Kalyani, Kalyani, India
  • fYear
    2010
  • fDate
    16-18 Dec. 2010
  • Firstpage
    344
  • Lastpage
    348
  • Abstract
    Identifying possible viral-host protein-protein interactions is an important and useful approach in developing new drugs targeting those interactions. In this article, a recently published dataset containing records of interactions between a set of HIV-1 proteins and a set of human proteins has been analyzed using association rule mining. The main objective is to identify a set of association rules among the human proteins with high confidence. The well-known Apriori algorithm has been utilized for discovering the association rules. Moreover, we have predicted some new viral-human interactions based on the discovered association rules.
  • Keywords
    bioinformatics; data mining; drugs; microorganisms; molecular biophysics; proteins; HIV-1 proteins; HIV-human protein interactions; a priori algorithm; association rule mining; drugs; viral-host protein-protein interactions; Association rules; Bioinformatics; Humans; Itemsets; Proteins; Apriori algorithm; HIV-1-human interaction; Protein-protein interaction; association rule mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems in Medicine and Biology (ICSMB), 2010 International Conference on
  • Conference_Location
    Kharagpur
  • Print_ISBN
    978-1-61284-039-0
  • Type

    conf

  • DOI
    10.1109/ICSMB.2010.5735401
  • Filename
    5735401