• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3543-3549
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• 2015

Background: Bladder cancer is one of the most common cancers worldwide. Gene expression profiling using microarray technologies improves the understanding of cancer biology. The aim of this study was to determine the gene expression profile in Egyptian bladder cancer patients. Materials and Methods: Samples from 29 human bladder cancers and adjacent non-neoplastic tissues were analyzed by cDNA microarray, with hierarchical clustering and multidimensional analysis. Results: Five hundred and sixteen genes were differentially expressed of which SOS1, HDAC2, PLXNC1, GTSE1, ULK2, IRS2, ABCA12, TOP3A, HES1, and SRP68 genes were involved in 33 different pathways. The most frequently detected genes were: SOS1 in 20 different pathways; HDAC2 in 5 different pathways; IRS2 in 3 different pathways. There were 388 down-regulated genes. PLCB2 was involved in 11 different pathways, MDM2 in 9 pathways, FZD4 in 5 pathways, p15 and FGF12 in 4 pathways, POLE2 in 3 pathways, and MCM4 and POLR2E in 2 pathways. Thirty genes showed significant differences between transitional cell cancer (TCC) and squamous cell cancer (SCC) samples. Unsupervised cluster analysis of DNA microarray data revealed a clear distinction between low and high grade tumors. In addition 26 genes showed significant differences between low and high tumor stages, including fragile histidine triad, Ras and sialyltransferase 8 (alpha) and 16 showed significant differences between low and high tumor grades, like methionine adenosyl transferase II, beta. Conclusions: The present study identified some genes, that can be used as molecular biomarkers or target genes in Egyptian bladder cancer patients.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.4
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• pp.310-316
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• 2020

Microarray technology represents a critical new advance in molecular cytogenetics. The development of this approach has provided fundamental insights into the molecular pathogenesis in clinical cytogenetics and has provided a clue to many unidentified or unexplained diseases. The approach allows a comprehensive investigation of thousands and millions of genomic loci simultaneously and enables the efficient detection of copy number alterations. The application of this technology has shown tremendous fluidity and complexity of the human genome, and has provided accurate diagnosis and appropriate clinical management in a timely and efficient manner for identifying genomic alterations. The clinical impact of the genomic alterations identified by microarrays is evolving into a diagnostic tool to identify high-risk patients better and predict patient outcomes from their genomic profiles. The transformation of conventional cytogenetics into an automated discipline will improve diagnostic yield significantly, leading to accurate diagnosis and genetic counseling. This article reviews cytogenetic technologies used to identify human chromosome alterations and highlights the potential utility of present and future genome microarray technology in the diagnosis.

• Journal of Microbiology and Biotechnology
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• v.11 no.6
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• pp.907-914
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• 2001

A whole genome analysis for monitoring specific changes in gene expression, using microarrays or proteome profiling of the same, are the two tools that have already revolutionized current approaches for studying disease. These methods are particularly important in cancer research as there are many overexpressed genes, and their products remain uncharacterized. This article presents a general overview of these technologies and their applications for studying cancer.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.41-44
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• 2000

With the advent of DNA microarray and "chip" technologies, gene expression in an organism can be monitored on a genomic scale, allowing the transcription levels of many genes to be measured simultaneously. Functional interpretation of massive expression data and linking such data to DNA sequences have become the new challenges to bioinformatics. I will us yeast cell cycle expression data analysis as an example to demonstrate how special database and computational methods may be used for extracting functional information, I will also briefly describe a novel clustering algorithm which has been applied to the cell cycle data.

• IMMUNE NETWORK
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• v.2 no.3
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• pp.125-132
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• 2002

In the past, bioinformatics was often regarded as a difficult and rather remote field, practiced only by computer scientists and not a practical tool available to biologists. However, the various on-going genome projects have had a serious impact on biological sciences in various ways and now there is little doubt that bioinformatics is an essential part of the research environment, with a wealth of biological information to analyze and predict. Fully sequenced genomes made us to have additional insights into the functional properties of the encoded proteins and made it possible to develop new tools and schemes for functional biology on a proteomic scale. Among those are the yeast two-hybrid system, mass spectrometry and microarray: the technology of choice to detect protein-protein interactions. These functional insights emerge as networks of interacting proteins, also known as "pathway informatics" or "interactomics". Without exception it is no longer possible to make advances in the signaling/regulatory pathway studies without integrating information technologies with experimental technologies. In this paper, we will introduce the databases of protein interaction worldwide and discuss several challenging issues regarding the actual implementation of databases.

• Interdisciplinary Bio Central
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• v.1 no.3
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• pp.10.1-10.5
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• 2009

Toxicity is usually investigated using a set of standardized animal-based studies which, unfortunately, fail to detect all compounds that induce human adverse events and do not provide detailed mechanistic information of observed toxicity. As an alternative to conventional toxicology, toxicogenomics takes advantage of currently advanced technologies in genomics, proteomics, metabolomics, and bioinformatics to gain a molecular level understanding of toxicity and to enhance the predictive power of toxicity testing in drug development and risk/safety assessment. In addition, there has been a renewed interest, particularly in various government agencies, to prioritize and/or supplement animal testing with a battery of mechanistically informative in vitro assays. This article provides a brief summary of the issues, challenges and lessons learned in these fields and discuss the ways forward to further advance toxicology using these technologies.

• Genomics & Informatics
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• v.13 no.3
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• pp.76-80
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• 2015

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

• 한국생물공학회:학술대회논문집
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• 2005.04a
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• pp.47-47
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• 2005

There are many different surface technologies currently applied for preparation of protein chips. However, it requires innovative surface chemistry for capture proteins to be immobilized on chip surface keeping their conformation and activity intact and their orientation right, while they bind tightly and densely in a given array spot. Proteogen has developed 'ProteoChip BP' coated with novel proprietary linker molecules $(ProLinker^{TM})$ for efficient and robust immobilizations of capture proteins by improving surface properties of molecular captures. It was demonstrated that $ProLinker^{TM}$ gave the best surface performance in preparation of protein microarray chip base plates among others currently available on the market. In particular, the $ProLinker^{TM}-based$ surface chemistry has demonstrated to provide excellent performance in preparation of 'Antibody Chip' for analysis of biomarkers as well as proteome expression profiles. The linker molecule has also shown to be well applicable for development of biosensors and micro-beads as well as protein microarray and nano-array. ProteoChip BP can be used either for preparation of high-density array by using a microarrayer or for preparation of 'Well-on-a-Chip' with low density array, which is better applicable for quantitative analysis of biomarkers or protein-protein interactions. The biomarker assay can be performed either by direct or sandwich methods of fluorescence immunoassay. Application of ProteoChip BP has been well demonstrated by the extensive studies of 1) tumor-marker assays, 2) new drug screening by using 'Integrin Chip' and 3) protein expression profile analysis. Some of experimental results will be presented.

• Journal of Microbiology and Biotechnology
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• v.19 no.7
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• pp.635-646
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• 2009

Rapid and accurate diagnosis of diseases is very important for appropriate treatment of patients. Recent advances in molecular-level interaction and detection technologies are upgrading the clinical diagnostics by providing new ways of diagnosis, with higher speed and accuracy. In particular, DNA microarrays can be efficiently used in clinical diagnostics which span from discovery of diseaserelevant genes to diagnosis using its biomarkers. Diagnostic DNA microarrays have been used for genotyping and determination of disease-relevant genes or agents causing diseases, mutation analysis, screening of single nucleotide polymorphisms (SNPs), detection of chromosome abnormalities, and global determination of posttranslational modification. The performance of DNA-microarray-based diagnosis is continuously improving by the integration of other tools. Thus, DNA microarrays will play a central role in clinical diagnostics and will become a gold standard method for disease diagnosis. In this paper, various applications of DNA microarrays in disease diagnosis are reviewed. Special effort was made to cover the information disclosed in the patents so that recent trends and missing applications can be revealed.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.74-82
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• 2000

Prostate cancer initially responds and regresses in response to androgen depletion therapy, but most human prostate cancers will eventually recur, and re-grow as an androgen independent tumor. Once these tumors become hormone refractory, they usually are incurable leading to death for the patient. Little is known about the molecular details of how prostate cancer cells regress following androgen ablation and which genes are involved in the androgen independent growth following the development of resistance to therapy. Such knowledge would reveal putative drug targets useful in the rational therapeutic design to prevent therapy resistance and control androgen independent growth. The application of genome scale technologies have permitted new insights into the molecular mechanisms associated with these processes. Specifically, we have applied functional genomics using high density cDNA microarray analysis for parallel gene expression analysis of prostate cancer in an experimental xenograft system during androgen withdrawal therapy, and following therapy resistance, The large amount of expression data generated posed a formidable bioinformatics challenge. A novel template based gene clustering algorithm was developed and applied to the data to discover the genes that respond to androgen ablation. The data show restoration of expression of androgen dependent genes in the recurrent tumors and other signaling genes. Together, the discovered genes appear to be involved in prostate cancer cell growth and therapy resistance in this system. We have also developed and applied tissue microarray (TMA) technology for high throughput molecular analysis of hundreds to thousands of clinical specimens simultaneously. TMA analysis was used for rapid clinical translation of candidate genes discovered by cDNA microarray analysis to determine their clinical utility as diagnostic, prognostic, and therapeutic targets. Finally, we have developed a bioinformatic approach to combine pharmacogenomic data on the efficacy and specificity of various drugs to target the discovered prostate cancer growth associated candidate genes in an attempt to improve current therapeutics.