SVM classifier based feature selection using GA, ACO and PSO for siRNA design

Authors:
Yamuna Prasad;K. Kanad Biswas;Chakresh Kumar Jain
Affiliations:
Department of Computer Science and Engineering, Indian Institute of Technology, Delhi, India;Department of Computer Science and Engineering, Indian Institute of Technology, Delhi, India;Department of Biotechnology, Jaypee Institute of Information Technology University, Noida, India
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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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.