• DocumentCode
    3600317
  • Title

    Machine learning predictions of cancer driver mutations

  • Author

    Jordan, E. Joseph ; Radhakrishnan, Ravi

  • Author_Institution
    Biochem. & Mol. Biophys. Grad. Group, Univ. of Pennsylvania, Philadelphia, PA, USA
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    A method to predict the activation status of kinase domain mutations in cancer is presented. This method, which makes use of the machine learning technique support vector machines (SVM), has applications to cancer treatment, as well as numerous other diseases that involve kinase misregulation.
  • Keywords
    bioinformatics; cancer; cellular biophysics; enzymes; genetics; learning (artificial intelligence); medical computing; patient treatment; support vector machines; tumours; SVM; activation status prediction; cancer driver mutations; cancer treatment; disease treatment; kinase domain mutations; kinase misregulation; machine learning predictions; machine learning technique; support vector machines; Accuracy; Bioinformatics; Genomics; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    In Silico Oncology and Cancer Investigation (IARWISOCI), 2014 6th International Advanced Research Workshop on
  • Type

    conf

  • DOI
    10.1109/IARWISOCI.2014.7034632
  • Filename
    7034632
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3600317