Gene Expression Networks to Analysis DNA Microarray Data

Unlike the reductionist views of classical biology، holistic approach in biology is shown with an explosion in the development of high-tech techniques to produce large amounts of data. Now the challenge for biologists is to discover ways to analyze this data in order to ability to support understanding the complex dynamic systems of life. In recent advanced technologies that are more public، DNA microarray are most famous. Microarray simultaneously examines expression levels of thousands of genes and provides a snapshot of the transcriptional activity of the cells in multiple conditions. Microarray have provided chance، especially in search of new territory، to describe the genes involved in biological processes such as cell cycle، growth and development of cell، assessment of chemical and genetic disorders and to identify genes associated with various diseases. Sheer volume of data produced by microarray studies need to develop advanced statistical analysis computer tools. In this study has been reviewed statistical methods based on graph theory. Construction the gene expression network (GCN)، GCN integration with other data، GCN analysis and application GCN for cancer research fully described. Finally we can say that study of the genome through microarray includes more samples and is possible in wide range of the species and in future applications of meta-analysis methods to integrate large amounts of data such as network alignment may help to clarify the similarities and differences between a wide range of species، tissues and disease states.