While capturing gene expressions using microarray technique missing values get generated in the data set. These missing values create negative impact on downstream analysis of DNA microarray. Therefore, it is necessary to estimate them before starting further analysis. Many algorithms are proposed for imputation of missing values which are based on statistical methods. They require complete gene expression data set which is created by replacing missing values by different methods like row averaging or column averaging and later missing expressions are imputed. This may affect efficiency of algorithms. In order to deal with problem of missing values, we have proposed new method based on Swarm Intelligence which is easy to implement and apply to any kind of dataset irrespective of amount of missing values in it. This method imputes missing gene expressions in microarray data set using Particle Swarm Optimization.
Genes contain blue print of living organism. Malfunctioning occurred in cellular life is indicated by proteins which are responsible for behavior of genes. Fixed set of genes decides behavior and functioning of cells. They guide the cells what to do and when to do. To analyze the insight of biological activities, analysis of gene expressions is necessary. Advanced technology like microarray plays an important role in gene analysis. It captures expressions of thousands of genes under different conditions simultaneously. Out of thousands of genes, very few behave differently which are called as Differentially Expressed Genes (DEGs). Identification of these most significant genes is a crucial task in molecular biology and is a major area of research for bioinformaticians because DEGs are the major source of disease prediction. They help in planning therapeutic strategies for a disease through Gene Regulatory Network (GRN) which is constructed from them. GRN is a graphical representation containing genes as nodes and regulatory interactions between them as edges. GRN helps in knowing how genes regulate each other and in sense maintain underlined state of art working of cells. Deregulation between genes is the cause of major genetic diseases. In this paper we have discussed many methods proposed by researchers for identifying differentially expressing genes based upon changes in their expressions patterns.
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