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SVM Classifier Based Feature Selection Using GA, ACO and PSO for siRNA Design

  • Yamuna Prasad
  • K. Kanad Biswas
  • Chakresh Kumar Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6146)

Abstract

Recently there has been considerable interest in applying evolutionary and natural computing techniques for analyzing large datasets with large number of features. In particular, efficacy prediction of siRNA has attracted a lot of researchers, because of large number of features involved. In the present work, we have applied the SVM based classifier along with PSO, ACO and GA on Huesken dataset of siRNA features as well as on two other wine and wdbc breast cancer gene benchmark dataset and achieved considerably high accuracy and the results have been presented. We have also highlighted the necessary data size for better accuracy in SVM for selected kernel. Both groups of features (sequential and thermodynamic) are important in the efficacy prediction of siRNA. The results of our study have been compared with other results available in the literature.

Keywords

siRNA ACO GA PSO LibSVM RBF 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yamuna Prasad
    • 1
  • K. Kanad Biswas
    • 1
  • Chakresh Kumar Jain
    • 2
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyDelhiIndia
  2. 2.Department of BiotechnologyJaypee Institute of Information Technology UniversityNoidaIndia

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