• Proceedings of the Korean Society of Toxicology Conference
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• 2000.11a
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• pp.31-41
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• 2000

The major issue in the post genome sequencing era is determination of gene expression patterns in variety of biological systems. A microarray system is a powerful technology for analyzing the expression profile of thousands of genes at one experiment. In this study, we constructed cDNA microarray which carries 2,304 cDNAS derived from oligo-capped mouse cDNA library. Using this hand-made microarray we determined gene expression in various biological systems. To determine tissue specific genes, we compared Nine genes were highly-expressed in adult mouse brain compared to kidney, liver, and skeletal muscle. Tissue distribution analysis using DNA microarray extracted 9 genes that were predominantly expressed in the brain. A database search showed that five of the 9 genes, MBP, SC1, HiAT3, S100 protein-beta, and SNAP25, were previously known to be expressed at high level in the brain and in the nervous system. One gene was highly sequence similar to rat S-Rex-s/human NSP-C, suggesting that the gene is a mouse homologue. The remaining three genes did not match to known genes in the GenBank/EMBL database, indicating that these are novel genes highly-expressed in the brain. Our DNA microarray was also used to detect differentiation specific genes, hormone dependent genes, and transcription-factor-induced genes. We conclude that DNA microarray is an excellent tool for identifying differentially expressed genes.

• 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.

• Journal of Oriental Neuropsychiatry
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• v.18 no.1
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• pp.95-110
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• 2007

Objective : The purpose of this study was to investigate molecular basis of neuroprotective effect in total ginsenosides. After H202 induced neurotoxicity, gene expression profiling of SH-SY5Y neuroblastoma cells treated by total ginsenosides is analyzed. Method : After SH-SY5Y cells were cultured, they were damaged by H202 induced oxidative stress. After twenty four hours, experimental group is treated by total ginsenosides and control group is treated by 0.9% saline. A high density cDNA microarray chip is used to analyze the gene expression profiling of SH-SY5Y cells. The Significance Analysis of Microarray method is used for identifying genes on a microarray. Results : 1. According to the results of microarray experiment, 17 genes were up-regulated, 38 genes were down-regulated. 2. Expression of OPHNl, KTANl, ATM, PRKCE, MAPKs genes associated with cell proliferation, neural growth, and the prevention of apoptosis were increased. 3. Change of EPX gene was the greatest among all genes. EPX gene associated with oxidative stress, and tumor suppressor gene ADAM11 were decreased. Conclusion : According to this study, molecular basis of neuroprotective effect of total ginsenosides is as followings: the increase of gene expression associated with cell proliferation, neuron growth, the prevention of apoptotsis and decrease of gene expression associated with oxidative stress and tumor suppressor.

• 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.

• 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 Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• KOGO NEWS
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• v.6 no.1
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• pp.29-33
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• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• 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.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.59-77
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• 2004

Since the introduction of DNA microarray, a revolutionary high through-put biological technology, a lot of papers have been published to deal with the analyses of the gene expression data from the microarray. In this paper we review most papers relevant to the cDNA microarray data, classify them in statistical methods' point of view, and present some statistical methods deserving consideration and future study.

• Molecular & Cellular Toxicology
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• v.5 no.1
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• pp.44-50
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• 2009

This study was conducted to evaluate the inflammation mechanisms of tumor necrosis factor-$\alpha$ (TNF-$\alpha$), interleukin-4 (IL-4), and IL-$1{\beta}$-induced stimulation of A549 human epithelial cells. In the present study, A549 cells were stimulated with TNF-$\alpha$, IL-4 and IL-$1{\beta}$ to induce expression of chemokines and adhesion molecules involved in eosinophil chemotaxis. The effects of TNF-$\alpha$, IL-4 and IL-$1{\beta}$ on gene expression profiles in A549 cells were evaluated by oligonucleotide microarray and Real time RT-PCR. The gene expression profiles for the A549 cells varied depending on the cytokines. Also, the results of the microarray and Real time RT-PCR revealed that inflammatory-related genes were up-regulated in cytokine stimulated A549 cells. Cytokines can affect inflammation in A549 cells. A microarray-based genomic survey is a high-throughput approach that enables evaluation of gene expression in cytokine stimulated cell lines.