Semi Supervised Based Spatial EM Framework for Microarray Analysis

Microarray technology has become one of the vital tools that many biologists use to monitor genome wide expression levels of genes in a given organism under a particular condition. A microarray is typically a glass slide on to which DNA molecules are fixed in an orderly manner at specific locations called spots (or features). This data can be represented as gene expression which can be constructed as table where each row corresponds to one particular gene, each column to a sample, and each entry of the matrix is the measured expression level of a particular gene in a sample, respectively. The main problem is analyzing gene expression to classify samples based on particular patterns among large amount genes. So in this paper, monitor large amount gene expression according to their gene expression profiles. This can be done using clustering approach with finite mixture of learning data to mine meaningful patterns from the gene expression data using Spatial EM algorithm. It can be used to calculate spatial mean and rank based scatter matrix to extract relevant patterns and further implement KNN (K- nearest neighbor classification) approach to diagnosis the diseases. The experimental results prove that Spatial EM based classification approach provides improved accuracy rate in disease diagnosis.