• 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.

• 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.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.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.6
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• pp.265-270
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• 2002

The determination of DNA hybridization reaction can apply the molecular biology research, clinic diagnostics, bioengineering, environment monitoring, food science and application area. So, the improvement of DNA hybridization detection method is very important for the determination of this hybridization reaction. Several molecular biological techniques require accurate predictions of matched versus mismatched hybridization thermodynamics, such as PCR, sequencing by hybridization, gene diagnostics and antisense oligonucleotide probes. In addition, recent developments of oligonucleotide chip arrays as means for biochemical assays and DNA sequencing requires accurate knowledge of hybridization thermodynamics and population ratios at matched and mismatched target sites. In this study, we report the characteristics of the probe and matched, mismatched target oligonucleotide hybridization reaction using thermodynamic method. Thermodynamic of 5 oligonucleotides with central and terminal mismatch sequences were obtained by measured UV-absorbance as a function of temperature. The data show that the nearest-neighbor base-pair model is adequate for predicting thermodynamics of oligonucleotides with average deviations for $\Delta$H$^{0}$ , $\Delta$S$^{0}$ , $\Delta$G$_{37}$ $^{0}$ and T$_{m}$, respectively.>$^{0}$ and T$_{m}$, respectively.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1117-1128
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• 2016

In genetic association studies with high-dimensional genomic data, regularization procedures based on penalized likelihood are often applied to identify genes or genetic regions associated with diseases or traits. A network-based regularization procedure can utilize biological network information (such as genetic pathways and signaling pathways in genetic association studies) with an outstanding selection performance over other regularization procedures such as lasso and elastic-net. However, network-based regularization has a limitation because cannot be applied to high-dimension genomic data with a group structure. In this article, we propose to combine data dimension reduction techniques such as principal component analysis and a partial least square into network-based regularization for the analysis of high-dimensional genomic data with a group structure. The selection performance of the proposed method was evaluated by extensive simulation studies. The proposed method was also applied to real DNA methylation data generated from Illumina Innium HumanMethylation27K BeadChip, where methylation beta values of around 20,000 CpG sites over 12,770 genes were compared between 123 ovarian cancer patients and 152 healthy controls. This analysis was also able to indicate a few cancer-related genes.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.598-600
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• 2001

DNA chip technology enables us to monitor thousands of gene expressions per sample simultaneously. Typically, DNA microarray data has at least several thousands of variables (genes) wish relatively smal1 number of samples. Thus feature (gene) selection by dimensionality reduction is necessary for efficient data analysis. In this paper we employ the partial least squares (PLS) method for gene selection and the principal component analysis (PCA) method for classification. The useful behavior of the PLS is verified by computer simulations.

• Korean Journal of Microbiology
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• v.41 no.2
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• pp.105-111
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• 2005

Streptomyces produces many kinds of secondary-metabolites including antibiotics. Screening of a new compound and elucidation of a biosynthetic pathway for the secondary metabolites are very important fields of biology, however, there is a main problem that most of the identified compounds are already researched compounds. To solve these problems, a microarray system that is based on the data related to the biosynthetic genes for secondary-metabolites was designed. For the main contents of DNA microarray, the important genes for the bio-synthesis of aminoglycosides, polyenes group, enediyne group, alpha-glucosidase inhibitors, glycopeptide group, and orthosomycin group were chosen. A DNA microarray with 69 genes that were involved in the bio-synthesis for the antibiotics mentioned above was prepared. The usability of the DNA microarray was confirmed with the chromosomal DNA and total RNA extracted from S. coelicolor whose genomic sequence had already been reported.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2283-2288
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• 2006

As development in technology of bioinformatics recently makes it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. Thus, DNA microarray technology presents the new directions of understandings for complex organisms. Therefore, it is required how to analyze the enormous gene information obtained through this technology effectively. In this thesis, We used sample data of bioinformatics core group in harvard university. It designed and implemented system that evaluate accuracy after dividing in class of two using Bayesian algorithm, ASA, of feature extraction method through normalization process, reducing or removing of noise that occupy by various factor in microarray experiment. It was represented accuracy of 98.23% after Lowess normalization.

• Proceedings of the Ginseng society Conference
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• 2004.05a
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• pp.17-28
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• 2004

As Korean ginseng is hybrid, an individual variation is very severe, and it takes long times in new breeding because it is required 4 years to pick the seed. But, transformation technique makes the high-functional breeding in short time. The focus of these ginseng studies is to find and secure the useful gene. And it is urgent to accumulate the fundamental data for the molecular breeding and secure the useful genes. Therefore, transformation and soil acclimatization technique are necessary to molecular breeding in use of the introduction of functional genes. In this study, it add to secure of new regulation gene and useful gene as to accumulate the fundamental data for the place where it will contribute to raise the national competitive power. To analyze the useful genes in large scale, we constructed CDNA libraries with various tissues, species, and treated tissue. EST analysis of ginseng perform in large scale and build the EST database of ginseng. We perform the full length sequencing about the selected lots of clones that include the entire open reading frame of the amino acid residues and construct cDNA chip with the parental EST clones. Establishment of the transformation and a soil acclimatization system throuth the re-introduction of the selected ginseng gene that related with the secondary metabolism and anti-stress into the ginseng.

• KSBB Journal
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• v.20 no.3
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• pp.220-227
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• 2005

Doxorubicin is an anthracycline-family polyketide compound with a very potent anti-cancer activity, typically produced by Streptomyces peucetius. To understand the potential target biosynthetic genes critical for the doxorubicin everproduction, a doxorubicin-specific DNA microarray chip was fabricated and applied to reveal the growth-phase-dependent expression profiles of biosynthetic genes from two doxorubicin-overproducing strains along with the wild-type strain. Two doxorubicin-overproducing 5. peucetius strains were generated via over-expression of a dnrl (a doxorubicin-specific positive regulatory gene) and a doxA (a gene involved in the conversion from daunorubicin to doxorubicin) using a streptomycetes high expression vector containing a strong ermE promoter. Each doxorubicin-overproducing strain was quantitatively compared with the wild-type doxorubicin producer based on the growth-phase-dependent doxorubicin productivity as well as doxorubicin biosynthetic gene expression profiles. The doxorubicin-specific DNA microarray chip data revealed the early-and-steady expressions of the doxorubicin-specific regulatory gene (dnrl), the doxorubicin resistance genes (drrA, drrB, drrC), and the doxorubicin deoxysugar biosynthetic gene (dnmL) are critical for the doxorubicin overproduction in S. peucetius. These results provide that the relationship between the growth-phase-dependent doxorubicin productivity and the doxorubicin biosynthetic gene expression profiles should lead us a rational design of molecular genetic strain improvement strategy.