• DocumentCode
    2736496
  • Title

    Invited: Multiclass RNA function classification using next-generation sequencing

  • Author

    Ryvkin, Paul ; Leung, Yuk Yee ; Wang, Li-San ; Gregory, Brian D.

  • Author_Institution
    Penn Center for Bioinf., Univ. of Pennsylvania, Philadelphia, PA, USA
  • fYear
    2011
  • fDate
    3-5 Feb. 2011
  • Firstpage
    10
  • Lastpage
    10
  • Abstract
    RNA-seq produces detailed information including length, strand and pairing states, which can be leveraged to characterize RNA functional categories using machine-learning approaches. Using fruit fly small-RNA-seq data, we demonstrate that by combining read length correlation with multi-class classifier models, we can classify four non-coding RNA function classes with high precision.
  • Keywords
    biology computing; molecular biophysics; molecular configurations; organic compounds; machine-learning approaches; multiclass RNA function classification; multiclass classifier models; next-generation sequencing; noncoding RNA function class; small-RNA-seq data; Accuracy; Bioinformatics; Feature extraction; Genomics; Next generation networking; RNA; Support vector machines; multi-class classification; next-generation RNA sequencing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    978-1-61284-851-8
  • Type

    conf

  • DOI
    10.1109/ICCABS.2011.5729859
  • Filename
    5729859
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2736496