• DocumentCode
    2960514
  • Title

    AudioGene: Computer-based prediction of genetic factors involved in non-syndromic hearing impairment

  • Author

    Taylor, Kyle R. ; DeLuca, Adam P. ; Goodman, Corey W. ; Tompkins, Bruce W. ; Scheetz, Todd E. ; Hildebrand, Michael S. ; Huygen, P.L.M. ; Smith, Richard J H ; Braun, Terry A. ; Casavant, Thomas L.

  • Author_Institution
    Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
  • fYear
    2011
  • fDate
    27-30 Dec. 2011
  • Firstpage
    75
  • Lastpage
    79
  • Abstract
    AudioGene is a software system developed at the University of Iowa to classify and predict gene mutations that indicate causal or increased risk factors of disease. We focus on a concise example - the most likely genetic causes of a particular form of inherited hearing loss - ADNSHL. Whereas the cost and throughput involved in the collection of genomic data have advanced dramatically during the past decade, gathering and interpreting clinical information regarding disease diagnosis remains slow, costly and error-prone. AudioGene employs machine-learning techniques in an iterative procedure to prioritize probable genetic risk factors of disease, which are then verified with a molecular (wet lab) assay. In our current implementation AudioGene achieves 67% first-choice accuracy (versus 23% using a majority classifier). When the top three choices are considered, accuracy increases to 83%. This has numerous implications for reducing the cost of genetic screening as well as increasing the power of novel gene discovery efforts. While AudioGene is focused on hearing loss, the design and underlying mechanisms are generalizable to many other diseases including heart disease, cancer and mental illness.
  • Keywords
    diseases; genetics; genomics; hearing; learning (artificial intelligence); medical information systems; molecular biophysics; pattern classification; ADNSHL; AudioGene; autosomal dominant nonsyndromic hearing loss; cancer; clinical information; computer-based prediction; disease diagnosis; gene discovery efforts; gene mutation prediction; genetic factors; genetic risk factors; genomic data collection; heart disease; iterative procedure; machine learning techniques; mental illness; nonsyndromic hearing impairment; software system; Accuracy; Auditory system; Diseases; Educational institutions; Genetics; Support vector machines; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Systems and Applications (AICCSA), 2011 9th IEEE/ACS International Conference on
  • Conference_Location
    Sharm El-Sheikh
  • ISSN
    2161-5322
  • Print_ISBN
    978-1-4577-0475-8
  • Electronic_ISBN
    2161-5322
  • Type

    conf

  • DOI
    10.1109/AICCSA.2011.6126605
  • Filename
    6126605