Identifying Differentially Expressed Genes in Different Stages of Lung Cancer – An Application of ARM Model on Gene Expression Data

Abstract

Changes in genes cause cancer by allowing cells to evade normal growth restrictions and become cancerous. Microarray technology has revolutionized the biomedical research. The purpose of microarray investigations is to find genes that are differently reproduced in cell cultures and samples under various biological circumstances. As a result various data mining and pattern recognition approaches, such as clustering, classification, and prediction, must be carefully reviewed when it comes to design data analysis methodologies. For a better knowledge of how genes are associated and how their dependencies vary from normal to cancerous stages, association rule mining tools can be used efficiently. In the present article we propose a method based on Apriori algorithm to identify candidate gene sets whose expression level significantly varies in different stages of lung cancer.