Article: Type-specific classification of bronchogenic carcinomas using bi-layer mutated particle swarm optimisation Journal: International Journal of Computer Aided Engineering and Technology (IJCAET) 2020 Vol.13 No.3 pp.360 - 370 Abstract: The cancer disease is posing a big challenge in the field of pathological diagnosis. The feature selection of cells is highly important in isolating the affected cells. The classification of cancer cells is gaining importance among clinical researchers. Gene expression profile (GEP) is used in better classifying genes in a cell or tissue. Gene expression data (GED) differs for every gene from which cell or tissue it is originated. Based on the GED the cancer cells can be classified into seven categories from which cell or tissue it was born. The infected cells can be graded from level one to four based on its growth and difference from other unaffected cells. Many techniques have been developed in the past for classifying cancer affected genes. In this paper we propose a modified classification algorithm bi-layer mutated particle swarm optimisation (BLMPSO). The microarray dataset used for testing the method is Affymetrix Human Genome <i>U</i>95<i>Av</i>2 Array. The simulation results showed that the proposed technique performs better in terms of classification based on GED than the other existing methods. Inderscience Publishers - linking academia, business and industry through research

Title: Type-specific classification of bronchogenic carcinomas using bi-layer mutated particle swarm optimisation

Authors: P. Balamurugan; A.M. Viswa Bharathy; K. Marimuthu; R. Niranchana

Addresses: Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, Tamilnadu, India ' Department of Computer Science and Engineering, Jyothismathi Institute of Technology and Science, Nustulapur, Karimnagar 505481, Telangana, India ' School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore-632014, Tamilnadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore-632014, Tamilnadu, India

Abstract: The cancer disease is posing a big challenge in the field of pathological diagnosis. The feature selection of cells is highly important in isolating the affected cells. The classification of cancer cells is gaining importance among clinical researchers. Gene expression profile (GEP) is used in better classifying genes in a cell or tissue. Gene expression data (GED) differs for every gene from which cell or tissue it is originated. Based on the GED the cancer cells can be classified into seven categories from which cell or tissue it was born. The infected cells can be graded from level one to four based on its growth and difference from other unaffected cells. Many techniques have been developed in the past for classifying cancer affected genes. In this paper we propose a modified classification algorithm bi-layer mutated particle swarm optimisation (BLMPSO). The microarray dataset used for testing the method is Affymetrix Human Genome U95Av2 Array. The simulation results showed that the proposed technique performs better in terms of classification based on GED than the other existing methods.

Keywords: cancer cells; feature selection; classification; gene expression; mutation; particle swarm optimisation; PSO.

DOI: 10.1504/IJCAET.2020.109520

International Journal of Computer Aided Engineering and Technology, 2020 Vol.13 No.3, pp.360 - 370

Received: 28 Dec 2017
Accepted: 02 Apr 2018
Published online: 11 Sep 2020 *

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Title: Type-specific classification of bronchogenic carcinomas using bi-layer mutated particle swarm optimisation

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