Background: MICROARRAY DNA technology has paved the way for investigators to expressed thousands of genes in a short time. ANALYSIS of this big amount of raw data includes normalization, clustering and classification. The present study surveys the application of clustering technique in MICROARRAY DNA ANALYSIS. Materials and methods: We analyzed data of Van’t Veer et al study dealing with BRCA1 and BRCA2 mutations in breast cancer. It was consisted of 18 patients with BRCA1 and 2 patients with BRCA2 mutation. Gene expression data were clustered using hierarchical and non-hierarchical approach. Then different clustering approaches were compared according to the actual classification with R software. Results: Hierarchical clustering showed a sensitivity of 94% and specificity of 100% in detecting BRCA1 gene. These figures were 89% and 100% for non-hierarchical clustering, respectively, indicating a satisfactory performance for both approaches. All clustering approaches classified sample No. 95 in BRCA2 group, however, clinical manifestations put her in BRCA1 group. Conclusion: With respect to satisfactory coincidence between clustering and actual classification results, clustering approach could be applied for cases when actual classification is missing.

Background: MICROARRAY technology is a powerful tool to study and analyze the behavior of thousands of genes simultaneously. Images of MICROARRAY have an important role in the detection and treatment of diseases. The aim of this study is to provide an automatic method for the extraction and ANALYSIS of MICROARRAY images to detect cancerous diseases.Methods: The proposed system consists of three main phases of image processing، data mining، and detection of disease. The image processing phase is accompanied with some operations such as identifying the location of genes، deleting the background، and extracting the raw data from the images. The second phase includes data normalization and selection of more effective genes. The disease is identified and recognized in the third phase using the extracted data.Results: In this study it has been used from breast cancer MICROARRAY images from Stanford University database. The accuracy of the proposed method to locate genes and diagnosis of breast cancer is up to 98 and 95.45%، respectively.Conclusion: The obtained results indicate that the proposed method is more accurate than other existing methods in MICROARRAY ANALYSIS. In addition، the proposed method is easily implemented and less costly compared to the clinical tests.

Introduction: Gene-set ANALYSIS of MICROARRAY data determines biological pathways or gene setswith differential expression in a phenotype of interest. In contrast to the ANALYSIS of individual genes, gene-set ANALYSIS utilizes existing biological knowledge of genes in assessing differential expression. This paper compared the biological performance of two gene-set ANALYSIS methods.Materials and Methods: To determinegene sets, which are differentially expressed between acute lymphoblastic leukemia (ALL) with BCR/ABL and those with no observed cytogenetic abnormalities, the real MICROARRAY data come from a clinical trial in acute lymphoblastic leukaemia were used in this study. For this reason, we used two GSA methods; GSEA-category and Global test and then the data were analyzed using by R software.Results: Globaltest identified 114 out of 200 gene sets introduced in KEGG with differentially expressed on comparing the group with BCR/ABL to those with no observed cytogenetic abnormalities. While Category could identify just 30 gene sets of this set.Conclusion: Both of used methods include number of gene sets affecting ALL. So the more thorough study is needed to identify the more metric method. Evaluation of common gene sets among the two methods could identify the latest findings of biologists only by the used statistical methods.

Aim: Aim of this study is screen of the large numbers of related genes of CD to find the key ones. Background: Celiac disease (CD) is known as a gluten sensitive and immune system dependent disease. There are several high throughput investigations about CD but it is necessary to clarify new molecular aspects mechanism of celiac. Methods: Whole-genome profile (RNA) of the human peripheral blood mononuclear cells (PBMCs) as Gene expression profile GSE113469 was retrieved Gene Expression Omnibus (GEO) database. The significant genes were selected and analyzed via proteinprotein interaction (PPI) network by Cytoscape software. The key genes were introduced and enriched via ClueGO to find the related biochemical pathways. Results: Among 250 significant genes 47 genes with expressed change above 2 fold change (FC) were interacted and the constructed network were analyzed. The network characterized by poor connections so it was promoted by addition 50 related nodes and 18 crucial nodes were introduced. Two clusters of biochemical pathways were identified and discussed. Conclusion: There is an obvious conflict between MICROARRAY finding and the well-known related genes of CD. This problem can be solve by more attention to the interpretation of PPI ntwork ANALYSIS results.

MICROARRAY technology has a wide usage in the fields of genomics and proteomics and enables us to monitor innumerable biological interactions simultaneously. This technology is very efficient and analyzes a lot of data in a short interval. The findings of MICROARRAY are then processed analyzed using bioinformatics. The basis of all MICROARRAYs is similar: there is a solid surface on which specific ligands are spotted. Then, a biological solution is poured on the surface and based on the binding of the ligand to the specific substance, the results are analyzed.

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 MICROARRAY 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 analyzed a large number of genes together along with the expected changes provides more closely examination the disease under study. In the present study, the DNA MICROARRAY data of leukemia disease were analyzed 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 leukemia gene sets were obtained from the database http: //www.biomedcentral.com/ content/ supplementary/1471and 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 identified 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 biological pathways (the database KEGG) were associated with the disease. Of 615 genes identified, 12 clusters were associated with the disease based on the functional annotation.Discussion & Conclusion: The results showed 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 markers for the diagnosis and treatment of leukemia disease.