Identification of Regulatory Binding Sites on mRNA Using in Vivo Derived Informations and SVMs

  • Carmen Maria Livi
  • Luc Paillard
  • Enrico Blanzieri
  • Yann Audic
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 154)

Abstract

Proteins able to interact with ribonucleic acids (RNA) are involved in many cellular processes. A detailed knowledge about the binding pairs is necessary to construct computational models which can avoid time consuming biological experiments. This paper addresses the creation of a model based on support vector machines and trained on experimentally validated data. The goal is the identification of RNA molecules binding specifically to a regulatory protein, called CELF1.

Keywords

Support vector machines bioinformatics machine learning classification RNA binding site prediction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carmen Maria Livi
    • 1
  • Luc Paillard
    • 2
  • Enrico Blanzieri
    • 1
  • Yann Audic
    • 2
  1. 1.Department of Computer Science DISIUniversity of TrentoTrentoItaly
  2. 2.CNRS, Institut genetique et developpement de RennesUniversity of Rennes 1RennesFrance

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