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An adaptive classification model for microarray analysis using big data

Biological procedures within cells are carried out by genes that can be studied using microarray technology. Gene expression data from a cell is stored and features of each cell are described by mercury. Microarray data is one of the latest breakthroughs used for selecting relevant biological information from thousands of genes simultaneously expressed in a distinct cell. Understanding the level of gene expression in a cell can provide valuable information in terms of identifying viral infection and determining cancer susceptibility. In a sample tissue, many genes are expressed. In that most of the genes are unrelated or irrelevant for clinical diagnosis. An accurate classification of diseases and phenotypes is selecting a small set of genes from thousands of genes. This paper concentrates on common gene expression classification techniques with MapReduce framework and related methods to find differentially expressed genes and select the significant genes in the specific gene expression.