• Genomics & Informatics
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• v.5 no.3
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• pp.129-132
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• 2007

arraylmpute is a software for exploratory analysis of missing data and imputation of missing values in microarray data. It also provides a comparative analysis of the imputed values obtained from various imputation methods. Thus, it allows the users to choose an appropriate imputation method for microarray data. It is built on R and provides a user-friendly graphical interface. Therefore, the users can easily use arraylmpute to explore, estimate missing data, and compare imputation methods for further analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.830-836
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• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.147-154
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• 2003

Microarray information system is a complex system to manage, analyze and interpretate microarray gene expression data. Establishment of well-defined development process is very essential for understanding the complexity and organization of the system. We performed object-oriented analysis using Unified Modeling Language (UML) in specifying, visualizing and documenting microarray information system. The object-oriented analysis consists of three major steps: (i) use case modeling to describe various functionalities from the user's perspective (ii) dynamic modeling to illustrate behavioral aspects of the system (iii) object modeling to represent structural aspects of the system. As a result of our modeling activities we provide the UML diagrams showing various views of the microarray information system. We believe that the object-oriented analysis ensures effective documentations and communication of information system requirements. Another useful feature of object-oriented technique is structural continuity to standard microarray data model MAGE-OM (Microarray Gene Expression Object Model). The proposed modeling e(forts can be applicable for integration of biomedical information system.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.89-106
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• 2009

There have been many new advances in the development of improved clustering methods for microarray data analysis, but traditional clustering methods are still often used in genomic data analysis, which maY be more due to their conceptual simplicity and their broad usability in commercial software packages than to their intrinsic merits. Thus, it is crucial to assess the performance of each existing method through a comprehensive comparative analysis so as to provide informed guidelines on choosing clustering methods. In this study, we investigated existing clustering methods applied to microarray data in various real scenarios. To this end, we focused on how the various methods differ, and why a particular method does not perform well. We applied both internal and external validation methods to the following eight clustering methods using various simulated data sets and real microarray data sets.

• 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.

• 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.

• 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 Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.98-107
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• 2006

Chromosomal copy number changes (aneuploidies) are common in human populations. The extra chromosome can affect gene expression by whole-genome level. By gene expression microarray analysis, we want to find aberrant gene expression due to aneuploidies in Klinefelter (+X) and Down syndrome (+21). We have analyzed the inactivation status of X-linked genes in Klinefelter Syndrome (KS) by using X-linked cDNA microarray and cSNP analysis. We analyzed the expression of 190 X-linked genes by cDNA microarray from the lymphocytes of five KS patients and five females (XX) with normal males (XY) controls. cDNA microarray experiments and cSNP analysis showed the differentially expressed genes were similar between KS and XX cases. To analyze the differential gene expressions in Down Syndrome (DS), Amniotic Fluid (AF)cells were collected from 12 pregnancies at $16{\sim}18$ weeks of gestation in DS (n=6) and normal (n=6) subjects. We also analysis AF cells for a DNA microarray system and compared the chip data with two dimensional protein gel analysis of amniotic fluid. Our data may provide the basis for a more systematic identification of biological markers of fetal DS, thus leading to an improved understanding of pathogenesis for fetal DS.