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Wolf-Swarm Colony for Signature Gene Selection Using Weighted Objective Method

Wolf-Swarm Colony for Signature Gene Selection Using Weighted Objective Method
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Author(s): Prativa Agarwalla (Heritage Institute of Technology, Kolkata, India) and Sumitra Mukhopadhyay (Institute of Radio Physics & Electronics, India)
Copyright: 2019
Pages: 26
Source title: Nature-Inspired Algorithms for Big Data Frameworks
Source Author(s)/Editor(s): Hema Banati (Dyal Singh College, India), Shikha Mehta (Jaypee Institute of Information Technology, India) and Parmeet Kaur (Jaypee Institute of Information Technology, India)
DOI: 10.4018/978-1-5225-5852-1.ch007

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Abstract

Microarray study has a huge impact on the proper detection and classification of cancer, as it analyzes the changes in expression level of genes which are strongly associated with cancer. In this chapter, a new weighted objective wolf-swarm colony optimization (WOWSC) technique is proposed for the selection of significant and informative genes from the cancer dataset. To extract the relevant genes from datasets, WOWSC utilizes four different objective functions in a weighted manner. Experimental analysis shows that the proposed methodology is very efficient in obtaining differential and biologically relevant genes which are effective for the classification of disease. The technique is able to generate a good subset of genes which offers more useful insight to the gene-disease association.

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