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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.

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Introduction: Leukemia is a progressive and malignant disease of hematopoietic organs of the body. Genetic abnormalities play an important role in the development of leukemia in the body. Many studies have been accomplished on the molecular factors that involved in the disease. DNA micro-array technology provides a general picture of gene expression in whole genome and applied for the exploring of candidate genes that lead to diseases. In fact, having analy-zed a large number of genes together along with the expected changes provides more closely examination the disease under stu-dy. In the present study, the DNA micro-array data of leukemia disease were analy-zed by bioinformatics software (DAVID). The aim of the study was to functionally analyze the genomic and proteomic lists of data that have been obtained with high-throughput tools during biological studies. Materials & Methods: Microarray leuke-mia gene sets were obtained from the data-base http://www.biomedcentral.com/ cont-ent/supplementary/1471 and analyzed with the bioinformatics software (DAVID). The communication gene expression in different classes, chart and clustered genes were examined. The list of genes was identified by the DAVID analysis program. Findings: A chart consisting of 615 ident-ified genes associated with various diseases was detected. Most genes involved in the disease were those genes that were also involved in cancers. 23.7% of the identified genes (146 genes) were cancer genes. Of 615 genes, 70 charts of the identified biolo-gical pathways (the database KEGG) were associated with the disease. Of 615 genes identified, 12 clusters were associated with the disease based on the functional annot-ation. Discussion & Conclusion: The results sho-wed that the program, DAVID, is capable of analyzing genome. Also, the program was capable to evaluate the main classes of genes and pathways involved in the disease to determine the best candidate gene mark-ers for the diagnosis and treatment of leuk-emia disease.