• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.53-62
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• 2006

DNA microarray data analysis is a new technology to investigate the expression levels of thousands of genes simultaneously. Since DNA 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 or that the data are stored to the file format. The existing analysis tools for DNA microarray data have many difficult problems by complicated instructions, and dependency on data types and operating system. In this paper, we design and implement network-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.

• KSBB Journal
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• v.22 no.6
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• pp.393-400
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• 2007

Analysis of protein interactions/functions in a microarray format has been of great potential in drug discovery, diagnostics, and cell biology, because it is amenable to large-scale and high-throughput biological assays in a rapid and economical way. In recent years, the protein microarray have broaden their utility towards the global analysis of protein interactions on a proteome scale, the functional activity analysis based on protein interactions and post-translational modifications (PTMs), and the discovery of biomarkers through profiling of protein expression between sample and reference pool. As a promising tool for proteomics, the protein microarray technology has advanced outstandingly over the past decade in terms of surface chemistry, acquisition of relevant proteins on a proteomic level, and detection methods. In this article, we briefly describe various techniques for development of protein microarray, and introduce developmental state of protein microarray and its applications.

• 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.

• 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.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.153-159
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• 2005

Microarray gene expression technology has applications that could refine diagnosis and therapeutic monitoring as well as improve disease prevention through risk assessment and early detection. Especially, microarray expression data can provide important information regarding specific genes related with metastasis through an appropriate analysis. Various methods for clustering analysis microarray data have been introduced so far. We used twostep clustering fot ascertain metastasis related gene through t-test. Through t-test between two groups for two publicly available medulloblastoma microarray data sets, we intended to find significant gene for metastasis. The paper describes the process in detail showing how the process is applied to clustering analysis and t-test for microarray datasets and how the metastasis-associated genes are explorated.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.37-41
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• 2005

Exploratory data analysis(EDA) is the initial stage of data analysis and provides a useful overview about the whole microarray experiment. If the experiments are replicated, the analyst should check the quality and reliability of microarray data within same experimental condition before the deeper statistical analysis. We shows EDA method focusing on the quality and reproducibility for replicates.

• Genomics & Informatics
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• v.5 no.2
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• pp.83-87
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• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform 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.

• Journal of Genetic Medicine
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• v.7 no.2
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• pp.111-118
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• 2010

Chromosomal microarray analysis (CMA) enables the genome-wide detection of submicroscopic chromosomal imbalances with greater precision and accuracy. In most other countries, CMA is now a commonly used clinical diagnostic test, replacing conventional cytogenetics or targeted detection such as FISH or PCR-based methods. Recently, some consensus statements have proposed utilization of CMA as a first-line test in patients with multiple congenital anomalies not specific to a well-delineated genetic syndrome, developmental delay/intellectual disability, or autism spectrum disorders. CMA can be used as an adjunct to conventional cytogenetics to identify chromosomal abnormalities observed in G-banding analysis in constitutional or acquired cases, leading to a more accurate and comprehensive assessment of chromosomal aberrations. Although CMA has distinct advantages, there are several limitations, including its inability to detect balanced chromosomal rearrangements and low-level mosaicism, its interpretation of copy number variants of uncertain clinical significance, and significantly higher costs. For these reasons, CMA is not currently a replacement for conventional cytogenetics in prenatal diagnosis. In clinical applications of CMA, knowledge and experience based on genetics and cytogenetics are required for data analysis and interpretation, and appropriate follow-up with genetic counseling is recommended.