• DocumentCode
    1605034
  • Title

    Finding Co-Clusters of Genes and Clinical Parameters

  • Author

    Yoon, Sungroh ; Benini, Luca ; De Micheli, Giovanni

  • Author_Institution
    Comput. Syst. Lab., Stanford Univ., CA
  • fYear
    2006
  • Firstpage
    906
  • Lastpage
    912
  • Abstract
    For better understanding of genetic mechanisms underlying clinical observations, we often want to determine which genes and clinical traits are interrelated. We introduce a computational method that can find co-clusters or groups of genes and clinical parameters that are believed to be closely related to each other based upon given empirical information. The proposed method was tested with data from an acute myelogenous leukemia (AML) study and identified statistically significant co-clusters of genes and clinical traits. The validation of our results with gene ontology (GO) as well as the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits
  • Keywords
    blood; cancer; cellular biophysics; genetics; medical computing; molecular biophysics; ontologies (artificial intelligence); acute myelogenous leukemia; clinical parameters; gene co-clusters; gene ontology; genetic mechanisms; DNA; Gene expression; Genetics; Large-scale systems; Monitoring; Ontologies; Testing; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
  • Conference_Location
    Shanghai
  • Print_ISBN
    0-7803-8741-4
  • Type

    conf

  • DOI
    10.1109/IEMBS.2005.1616562
  • Filename
    1616562