• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• Journal of Computing Science and Engineering
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• v.4 no.4
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• pp.350-367
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• 2010

Microarray technology is an interdisciplinary technique that promises a revolutionary progress toward better health and improved quality of life. The paper focuses on the development of an efficient software package, equipped with already well-known methods; also some new methods are proposed that will allow the processing and analysis of thousands of genes on microarray images. The microarray analysis software package (called SmartArray), newly proposed in this paper verifies, through microarray analysis, dramatic changes in the mRNA, protein, and activity level in the rat retina during light deprivation, which have been demonstrated in previous biological experiments. The analysis results demonstrate that SmartArray can successfully find many changes in gene expression levels in each subarray and classify them according to their significance.

• Biomedical Science Letters
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• v.20 no.4
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• pp.185-193
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• 2014

Cancers are based upon an array of orchestrated genetic changes and the identification of changes causally related to the carcinogenic process. To elucidate the mechanism of cancer carcinogenesis, it is necessary to reconstruct these molecular events at each level. Microarray technologies have been extensively used to evaluate genetic alterations associated with cancer onset and progression in clinical oncology. The clinical impact of the genomic alterations identified by microarray technologies are growing rapidly and array analysis has been evolving into a diagnostic tool to better identify high-risk patients and predict patient outcomes from their genomic profiles. Here, we discuss the state-of-the-art microarray technologies and their applications in clinical oncology, and describe the potential benefits of these analysis in the clinical implications and biological insights of cancer biology.

• Journal of the Korean Chemical Society
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• v.56 no.4
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• pp.530-534
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• 2012

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.845-848
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• 2004

High throughput analysis using a DNA chip microarray is powerful tool in the post genome era. Less labor-intensive and lower cost-performance is required. Thus, this paper aims to develop the multi-channel type label-free DNA chip and detect SNP (Single nucleotide polymorphisms). At first, we fabricated a high integrated type DNA chip array by lithography technology. Various probe DNAs were immobilized on the microelectrode array. We succeeded to discriminate of DNA hybridization between target DNA and mismatched DNA on microarray after immobilization of a various probe DNA and hybridization of label-free target DNA on the electrodes simultaneously. This method is based on redox of an electrochemical ligand.

• Molecules and Cells
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• v.22 no.3
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• pp.254-261
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• 2006

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.30-36
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• 2006

Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathological information related to TMA data analysis. This study aims to create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathological data elements. We designed a Tissue Microarray Object Model (TMA-OM). Both the Array Information and the Experimental Procedure models are created by referring to Microarray Gene Expression Object Model, Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE), and the TMA Data Exchange Specifications (TMA DES). The Clinical and Histopathological Information model is created by using CAP Cancer Protocols and National Cancer Institute Common Data Elements (NCI CDEs). MGED Ontology, UMLS and the terms extracted from CAP Cancer Protocols and NCI CDEs are used to create a controlled vocabulary for unambiguous annotation. We implemented a web-based application for TMA-OM, supporting data export in XML format conforming to the TMA DES or the DTD derived from TMA-OM. TMA-OM provides a comprehensive data model for storage, analysis and exchange of TMA data and facilitates model-level integration of other biological models.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.125-126
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• 2006

NimbleGen has developed strategies to use its high-density oligonucleotide microarray platform (385,000 probes per array) to map both promoter binding sites and copy number variation at very high-resolution in the human genome. Here we describe a genome-wide map of active promoters determined by experimentally locating the sites of transcription imitation complex binding throughout the human genome using microarrays combined with chromatin immunoprecipitation. This map defines 10,567 active promoters corresponding to 6,763 known genes and at least 1,196 un-annotated transcriptional units. Microarray-based comparative genomic hybridisation (CGH) is animportant research tool for investigating chromosomal aberrations frequently associated with complex diseases such as cancer, neuropsychiatric disorders, and congenital developmental disorders. NimbleGen array CGH is an ultra-high resolution (0.5-50 Kb) oligo array platform that can be used to detect amplifications and deletions and map the associated breakpoints on the whole-genome level or with custom fine-tiling arrays. For whole-genome array CGH, probes are tiled through genic and intergenic regions with a median probe spacing of 6 Kb, which provides a comprehensive, unbiased analysis of the genome.

• KIEE International Transactions on Electrophysics and Applications
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• v.11C no.2
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• pp.23-27
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• 2001

Microarray-based DNA chips provide an architecture for multi-analyte sensing. In this paper, we report a new approach for DNA chip microarray fabrication. Multifunctional DNA chip microarrays were made by immobilizing many kinds if DNAs on transducers (particles). DNA chip microarrays were prepared by randomly distributing a mixture of the particles on a chip pattern containing thousands of micro meter-scale sites. The particles occupied different sites from array to array. Each particle cam be distinguished by a tag that is established on the particle. The particles were arranged on the chip pattern by the random fluidic self-assembly (RFSA) method, using hydrophobic interaction.