A Hybrid Optimization Approach for Breast Cancer Gene Sequence Analysis Using Population based Soft Computing Techniques

K. Lohitha Lakshmi, P. Bhargavi and S. Jyothi

Among various cancers detecting techniques gene sequence analysis is also becoming more focused due to growing necessity of preventing and spreading of chronic genetic diseases. Among cancers breast cancer is identified as one of the chronic genetic disease mostly triggered in woman. Sequence analysis is used to identify the most contrasting properties of cancerous and normal tissue with the aid of standard population based optimal search techniques like differential evolution and particle swarm optimization rooted in soft computing by using different optimal test functions with different data sets of simulation experiments. In analyzing the gene sequences Multiple Sequence Alignment [MSA] made foremost effort to form optimal alignment sequences of certain classification. In the present paper different optimal test functions are applied in different population based stochastic search methodologies of soft computing to generate optimal values. These generated values used to discriminate normal and diseased tissues. One hybrid approach is proposed by using existing functions to enhance the performance boundaries. The resultant values of proposed hybrid approach are compared with the values of previous existing methods. CCS Concepts • Soft computing, Gene sequence analysis.

Volume 11 | Issue 11

Pages: 132-141

DOI: 10.5373/JARDCS/V11I11/20193178