• DocumentCode
    3029082
  • Title

    Support Vector Machine for Classification of Plants and Animal miRNA

  • Author

    Pant, Bhasker ; Pant, Kumud ; Pardasani, K.R.

  • Author_Institution
    Dept. of Bioinf., MANIT, Bhopal, India
  • fYear
    2009
  • fDate
    28-29 Dec. 2009
  • Firstpage
    338
  • Lastpage
    340
  • Abstract
    MicroRNAs (miRNAs) constitute a large family of non coding RNAs that function to regulate gene expression. Wet lab experiments usually used to classify the miRNA of plants and animals are highly expensive, labor intensive and time consuming. Thus there arises a need for computational approach for classification of plant and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. The new SVM learning algorithm called Weka LibSVM has been used for classification of plant and animal miRNA. The model has been tested on available data and it gives results with 95% accuracy.
  • Keywords
    agricultural engineering; biocomputing; learning (artificial intelligence); support vector machines; Weka LibSVM learning algorithm; animal miRNA classification; gene expression regulation; microRNA; plant miRNA classification; support vector machine; Animals; Bioinformatics; Cardiac disease; Drugs; Genomics; Organisms; RNA; Support vector machine classification; Support vector machines; Testing; Hyperplane; Kernel; MicroRNAs; Support Vector Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on
  • Conference_Location
    Trivandrum, Kerala
  • Print_ISBN
    978-1-4244-5321-4
  • Electronic_ISBN
    978-0-7695-3915-7
  • Type

    conf

  • DOI
    10.1109/ACT.2009.90
  • Filename
    5376655