• Proceedings of the Korean Society of Life Science Conference
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• 2000.06a
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• pp.3-8
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• 2000

The complete genome sequence of a Gram-positive bacterium .Bacillus subtilis has recently been reported and it is now clear that more than 50% of its ORFs have no known function (1). To study the global gene expression in B. subtilis at single gene resolution, we have tested the glass DNA microarrays in a step-wise fashion. As a preliminary experiment, we have created arrays of PCR products for 14 ORF whose transcription patterns have been well established through transcriptional mapping analysis. We measured changes in mRNA transcript levels between early exponential and stationary phase by hybridizing fluorescently labeled cDNA (with Cy3-UTP and Cy5-UTP) onto the array. We then compared the microarray data to confirm that the transcription patterns of these genes are well consistent with the known Northern analysis data. Since the preliminary test has been successful, we scaled up the experiments to ${\sim}$94% of the 4,100 annotated ORFs for the complete genome sequence of B. subtilis. Using this whole genomic microarray, we searched genes that are catabolite-repressive and those that are under the control of ${\sigma}^{Y}$, one of the functionally unknown ECF sigma factors. From these results, we here report that we have established DNA microarray techniques that are applicable for the whole genome of B. subtilis.

• Journal of Life Science
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• v.17 no.8 s.88
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• pp.1115-1120
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• 2007

In the present study, we investigated whether several phytochemicals (resveratrol, genistein, epicatechin gallate, dially disulfide, caffeic acid phenetyl ester) and sulindac sulfide could induce expression of tumor suppressor p53 protein in human colorectal HCT116 cells. We found that p53 was dramatically induced by all phytochemical treatments except sulindac sulfide. Among treated phytochemicals, we selected resveratrol for further experiments because it is one of the highest p53 inducer. Using a Western blot analysis, we found that resveratrol induced p53 in a dose- and time-dependent manner. Additionally, using membrane-based microarray analysis, we found that twenty-five genes were up-regulated and two genes were down-regulated by resveratrol treatment. Among the up-regulated genes, we selected 4 genes and performed reverse-transcription-PCR to confirm microarray data. The results of RT-PCR were highly accorded with those of membrane microarray. In addition, we found that thrombospondin-1 (TSP-1) expression was not dependent on p53 presence, whereas mammary serine protease inhibitor (MASPIN) expression was dependent on p53 expressed by resveratrol treatment. The results of this study may help to promote our understandings of the molecular mechanisms of chemoprevention that are mediated by resveratrol in human colorectal cancer.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.175-181
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• 2002

cDNA microarray experiments permit us to investigate the expression levels of thousands of genes simultaneously and to make it easy to compare gene expression from different populations. However, researchers are asked to be cautious in interpreting the results because of the unexpected sources of variation such as systematic errors from the microarrayer and the difference of cDNA dye intensity. And the scanner itself calculates both of mean and median of the signal and background pixels, so it follows a selection which raw data will be used in analysis. In this paper, we compare the results in each case of using mean and median from the raw data and normalization methods in reducing the systematic errors with arm's skin cells of old and young males. Using median is preferable to mean because the distribution of the test statistic (t-statistic) from the median is more close to normal distribution than that from mean. Scaled print tip normalization is better than global or lowess normalization due to the distribution of the test-statistic.

• Quantitative Bio-Science
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• v.36 no.1
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• pp.23-31
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• 2017

Datasets from DNA microarray experiments, which are in the form of large matrices of expression levels of genes, often have missing values. However, the existing statistical methods including the principle components analysis (PCA) and Hotelling's t-test are not directly applicable for the datasets having missing values due to the fact that they assume the observed dataset is complete in general. Many methods have been proposed in previous literature to impute the missing in the observed data. Troyanskaya et al. [1] study the k-nearest neighbor (kNN) imputation, Kim et al. [2] propose the local least squares (LLS) method and Rubin [3] propose the multiple imputation (MI) for missing values. To identify differentially expressed genes, we propose a new testing procedure when the missing exists in the observed data. The proposed procedure uses the Stouffer's z-scores and combines the test results of individual imputed samples, which are dependent to each other. We numerically show that the proposed test procedure based on MI performs better than the existing test procedures based on single imputation (SI) by comparing their ROC curves. We apply the proposed method to analyzing a public microarray data.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.131-140
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• 2010

Biclustering is a method of finding meaningful subsets of objects and attributes simultaneously, which may not be detected by traditional clustering methods. It is popularly used for the analysis of microarray data representing the expression levels of genes by conditions. Usually, biclustering algorithms do not consider a sequential relation between attributes. For time series data, however, bicluster solutions should keep the time sequence. This paper proposes a new biclustering algorithm for time series data by modifying the plaid model. The proposed algorithm introduces a parameter controlling an interval between two selected time points. Also, the pruning step preventing an over-fitting problem is modified so as to eliminate only starting or ending points. Results from artificial data sets show that the proposed method is more suitable for the extraction of biclusters from time series data sets. Moreover, by using the proposed method, we find some interesting observations from real-world time-course microarray data sets and apartment price data sets in metropolitan areas.

• The Journal of Korean Medicine
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• v.35 no.4
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• pp.36-51
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• 2014

Objectives: cDNA microarray is an effective method to snapshot gene expression. Functional clustering of gene expressions can identify herbal medicine mechanisms. Much microarray data is available for various herbal medicines. This study compares regulated genes with herbal medicines to evaluate the nature of the drugs. Methods: Published microarray data were collected. Total RNAs were prepared from dissociated hippocampal dissociate cultures which were given hypoxic shock in the presence of each herbal medicine. Up- or downregulated genes higher than Global M value 0.5 were selected, clustered in functional groups, and compared with various herbal treatments. Results: 1. Akt2 was upregulated by Acorus gramineus SOLAND, Arisaema amurense var. serratum $N_{AKAI}$ and Coptis chinensis $F_{RANCH}$, and they belong to Araceae herb. 2. Nf-${\kappa}b1$, Cd5, $Gn{\gamma}7$ and Sgne1 were upregulated by Arisaema amurense var. serratum $N_{AKAI}$, Coptis chinensis $F_{RANCH}$ and Rheum coreanum $N_{AKAI}$. 3. Woohwangcheongsim-won, Sohaphyang-won and Scutellaria baicalensis $G_{EORGI}$ downregulated Scp2 and upregulated Tsc2. Woohwangcheongsim-won and Sohaphyang-won upregulated Hba1 and downregulated Myf6. 4. Sohaphyang-won and Scutellaria baicalensis $G_{EORGI}$ downregulated Slc12a1. 5. Woohwangcheongsim-won and Arisaema amurense var. serratum $N_{AKAI}$ upregulated $Rar{\alpha}$, Woohwangcheongsim-won and Coptis chinensis $F_{RANCH}$ downregulated Rab5a and $Pdgfr{\alpha}$, and Woohwangcheongsim-won and Rheum coreanum $N_{AKAI}$ upregulated $Plc{\gamma}1$ and downregulated Pla2g1b and Slc10a1. Conclusions: By clustering microarray, genes are commonly identified to be either up- or downregulated. These results will provide new information to understand the efficacy of herbal medicines and to classify them at the molecular level.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.471-475
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• 2008

Microarray technology in biological science enables molecular level observations and analyses on the biological phenomina by allowing to measure the RNA expression profiles in cells. Microarray data analysis is applied in various purposes such as identifying significant genes which react to drug treatment, understanding the genome scale phenomina. In drug response experiments, the microarray-based gene expression analysis could provide meaningful information. It is sometimes needed to identify the genes which shows different expression behavior for treatment group and normal group each other. When the normal group shows the medium level expression, it is not easy to discriminate the group just by expression level comparison. This paper proposes a method which selects group-wise representative values for each gene and sets the value range of the groups in order to filter out the genes with specific pattern. It also shows some experiment results.

• Genomics & Informatics
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• v.4 no.3
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• pp.129-132
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• 2006

Toxicogenomics has recently emerged in the field of toxicology and the DNA microarray technique has become common strategy for predictive toxicology which studies molecular mechanism caused by exposure of chemical or environmental stress. Although microarray experiment offers extensive genomic information to the researchers, yet high dimensional characteristic of the data often makes it hard to extract meaningful result. Therefore we developed toxicant enrichment analysis similar to the common enrichment approach. We also developed web-based system graPT to enable considerable prediction of toxic endpoints of experimental chemical.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.733-735
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• 2003

DNA Microarray 기술은 유전자의 발현여부를 매우 빠르게 검사할 수 있는 도구이며 각종 질병의 발생여부를 예측하기 위한 정보를 제공한다. 유전자 발현 데이터로부터 암의 발생 여부를 예측하기 위해서는 기존의 접근방법과 다른 기계학습 기법이 요구된다. 일반적으로 샘플의 개수가 극히 적은 반면에 특징의 개수는 수천에서 수만 개가 존재하기 때문에 문제의 특성에 맞는 분류기의 구조를 결정하는 것이 매우 어려운 일이기 때문이다. 진화 신경망은 신경망의 구조와 가중치를 동시에 학습하며 사용자는 각 개체의 적합도를 평가할 수 있는 방법만 제공해 주면된다. 특히 신경망의 구조를 사전에 고정하지 않아도 되는 장점이 있기 때문에 전문적인 지식이 없는 사용자라도 이용가능하다. 대장암 데이터에 대한 실험결과 제안하는 분류기 모델이 다층 퍼셉트론, SVM (support vector machine), 최근접 이웃 방법에 비해 향상된 성능을 보였다.

• The Korea Genome Conference
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• 2003.09a
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• pp.53.2-53.2
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• 2003