• Journal of Pharmacopuncture
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• v.7 no.2
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• pp.19-28
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• 2004

봉독은 관절염 치료를 비롯한 여러 질환에 그 응용범위가 넓어지고 있으며 기전규명과 새로운 치료효과 개발을 위한 연구가 필요하다. 연골의 파괴는 진행된 각종 관절병증의 공통 병리기전이며 연골세포의 기능이상은 이 기전에 중요한 의미를 지닌다. 사람 연골세포의 특성을 유지하고 있는 HTB-94 연골육종세포를 배양하고 봉독을 처치했을 때의 유전자 발현양상을 microarray를 이용하여 관찰하였다. 대조군에 비해 4배 이상 발현의 차이가 있는 경우를 유의한 것으로 보았을 때 microarray의 344개 유전자중 봉독처치시 발현이 증강되는 유전자는 없었으며 발현이 억제되는 유전자는 interleukin 6 receptor, interleukin 1 alpha, tissue inhibitor of metalloproteinase 1, matrix metalloproteinase 1, tumor necrosis factor (ligand) superfamily, members 4, 8 and 12, and caspases 2, 6, and 10등 35개가 관찰되었다. Microarray를 통한 유전자발현 분석을 통해 관절염에 대한 봉독치료의 기전을 시사하는 유용한 자료를 얻을 수 있었으며 앞으로 보다 넓은 범위에 대한 연구가 필요할 것이다.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.315-321
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• 2011

Gene expression microarray data often include multiple missing values. Most gene expression analysis (including gene clustering analysis); however, require a complete data matric as an input. In ordinary clustering methods, just a single missing value makes one abandon the whole data of a gene even if the rest of data for that gene was intact. The quality of analysis may decrease seriously as the missing rate is increased. In the opposite aspect, the imputation of missing value may result in an artifact that reduces the reliability of the analysis. To clarify this contradiction in microarray clustering analysis, this paper compared the accuracy of clustering with and without imputation over several microarray data having different missing rates. This paper also tested the clustering efficiency of several imputation methods including our propose algorithm. The results showed it is worthwhile to check the clustering result in this alternative way without any imputed data for the imperfect microarray data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1161-1167
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• 2006

Since microarray data structures are various and complicative, the data are generally stored in databases for approaching to and controlling the data effectively. But we have some difficulties to analyze and control the data when the data are stored in the several database management systems. The existing analysis tools for DNA microarray data have many difficult problems by complicated instructions, and dependency on data types and operating system, and high cost, etc. In this paper, we design and implement the web-based analysis tool for obtaining to useful information from DNA microarray data. When we use this tool, we can analyze effectively DNA microarray data without special knowledge and education for data types and analytical methods.

• Genomics & Informatics
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• v.3 no.1
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• pp.39-42
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• 2005

DNA microarray is a high-throughput biomedical technology that monitors gene expression for thousands of genes in parallel. The abundance and complexity of the gene expression data have given rise to a requirement for their systematic management and analysis to support many laboratories performing microarray research. On these demands, we developed Xperanto for integrated data management and analysis using user-friendly web-based interface. Xperanto provides an integrated environment for management and analysis by linking the computational tools and rich sources of biological annotation. With the growing needs of data sharing, it is designed to be compliant to MGED (Microarray Gene Expression Data) standards for microarray data annotation and exchange. Xperanto enables a fast and efficient management of vast amounts of data, and serves as a communication channel among multiple researchers within an emerging interdisciplinary field.

• Molecules and Cells
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• v.25 no.2
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.419-430
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• 2004

Since cDNA microarray experiments can monitor expression levels for thousands of genes simultaneously, the experimental designs and their analyzing methods are very important for successful analysis of microarray data. The loop design is discussed for selecting differentially expressed genes among several treatments and the analysis of variance method is introduced to normalize microarray data and provide estimates of the interesting quantities. MA-ANOVA is used to illustrate this method on a recently collected loop designed microarray data at Cancer Metastasis Research Center, Yonsei University.

• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.271-274
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• 2004

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 microarray was made by immobilizing many kinds of biomaterials on transducers (particles). DNA chip microarray was prepared by randomly distributing a mixture of the particles on a chip pattern containing thousands of m-scale sites. The particles occupied a different sites from site to site. The particles were arranged on the chip pattern by the random fluidic self-assembly (RFSA) method, using a hydrophobic interaction for assembly.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.472-475
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• 2004

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 microarray was made by immobilizing many kinds of biomaterials on transducers (particles). DNA chip microarray was prepared by randomly distributing a mixture of the particles on a chip pattern containing thousands of m-scale sites. The particles occupied a different sites from site to site. The particles were arranged on the chip pattern by the random fluidic self-assembly (RFSA) method, using a hydrophobic interaction for assembly.

• Journal of Genetic Medicine
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• v.15 no.2
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• pp.49-54
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• 2018

Nuchal translucency is an important indicator of an aneuploid fetus in prenatal diagnostics. Previously, only the presence of aneuploid could be confirmed by conventional karyotyping of fetuses with thick nuchal translucency. With the development of genetic diagnostic techniques, however, it has been reported that subtle variations not detectable by conventional karyo-typing might occur in cases of pathologic clinical syndrome in euploid fetuses. One of the newer, high-resolution genetic methods in the prenatal setting is chromosomal microarray. The possible association between nuchal translucency thickness with normal karyotype and submicroscopic chromosomal abnormalities detectable by microarray has been studied. How and when to apply microarray in clinical practice, however, is still debated. This article reviews the current studies on the clinical application of microarray in cases of increased nuchal translucency with normal karyotype for prenatal diagnosis.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2001.11a
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• pp.110-110
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• 2001