• Journal of Life Science
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• v.22 no.10
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• pp.1359-1370
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• 2012

Arabidopsis AtERF71/HRE2, a transcription activator, is located in the nucleus and is involved in the signal transduction of low oxygen and osmotic stresses. In this study, microarray analysis using AtERF71/HRE2-overexpressing transgenic plants was performed to identify genes downstream of AtERF71/HRE2. A total of 161 different genes as well as AtERF71/HRE2 showed more than a twofold higher expression in AtERF71/HRE2-overexpressing transgenic plants compared with wild-type plants. Among the 161 genes, 24 genes were transcriptional regulators, such as transcription factors and DNA-binding proteins, based on gene ontology annotations, suggesting that AtERF71/HRE2 is an upstream transcription factor that regulates the activities of various downstream genes via these transcription regulators. RT-PCR analysis of 15 genes selected out of the 161 genes showed higher expression in AtERF71/HRE2-overexpressing transgenic plants, validating the microarray data. On the basis of Genevestigator database analysis, 51 genes among the 161 genes were highly expressed under low oxygen and/or osmotic stresses. RT-PCR analysis showed that the expression levels of three genes among the selected 15 genes increased under low oxygen stress and another three genes increased under high salt stress, suggesting that these genes might be downstream genes of AtERF71/HRE2 in low oxygen or high salt stress signal transduction. Microarray analysis results indicated that AtERF71/HRE2 might also be involved in the responses to other abiotic stresses and also in the regulation of plant developmental processes.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.13-32
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• 2006

Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression difference is also very important to cluster for microarray data. Thus, this paper present the results of broad comparative studies to time course microarray data by considering methods of gene selection, clustering and cluster validation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.1-7
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• 2005

Cluster analysis of microarray data has been often used to find biologically relevant Broups of genes based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this Paper we address a novel clustering approach, called seed clustering, and investigate its applicability for microarray data analysis. In the seed clustering method, seed genes are first extracted by computational analysis of their expression profiles and then clusters are generated by taking the seed genes as prototype vectors for target clusters. Since it has strong mathematical foundations, the seed clustering method produces the stable and consistent results in a systematic way. Also, our empirical results indicate that the automatically extracted seed genes are well representative of potential clusters hidden in the data, and that its performance is favorable compared to current approaches.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.623-631
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• 2011

A major goal of time-course microarray data analysis is the detection of groups of genes that manifest similar expression patterns over time. The corresponding numerous cluster algorithms for clustering time-course microarray data have been developed. In this study, we proposed a clustering method based on the primary pharmacokinetic parameters in the pharmacokinetics study for assessment of pharmaceutical equivalents between two drug products. A real data and a simulation data was used to demonstrate the usefulness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.569-574
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• 2011

In this paper, we introduce the subspace contrasting group identification problem and propose an algorithm to solve the problem. In order to identify contrasting groups, the algorithm first determines two groups of which attribute values are in one of the contrasting ranges specified by the analyst, and searches for the contrasting groups while increasing the dimension of subspaces with an association rule mining strategy. Because the dimension of microarray data is likely to be tens of thousands, it is burdensome to find all contrasting groups over all possible subspaces by query generation. It is very useful in the sense that the proposed method allows to find those contrasting groups without analyst's involvement.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.544-555
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• 2003

Since the result data from DNA microarray experiments contain a lot of gene expression information, adequate analysis methods are required. Hierarchical clustering is widely used for analysis of gene expression profiles. In this paper, we study leaf-ordering, which is a post-processing for the dendrograms output by hierarchical clusterings to improve the efficiency of DNA microarray data analysis. At first, we analyze existing leaf-ordering algorithms and then present new approaches for leaf-ordering. And we introduce a software HCLO(Hierarchical Clustering ＆ Leaf-Ordering Tool) that is our implementation of hierarchical clustering, some of existing leaf-ordering algorithms and those presented in this paper.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.585-594
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• 2009

Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.2032-2037
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• 2011

Marker genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect marker genes that are selected by ranking genes according to statistics after normalizing data with methods that are the most widely used among several normalization methods proposed the while, And it compare and analyze a performance of each of normalization methods with mult-perceptron neural network layer. The Result that apply Multi-Layer perceptron algorithm at Microarray data set including eight of marker gene that are selected using ANOVA method after Lowess normalization represent the highest classification accuracy of 99.32% and the lowest prediction error estimate.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.81-89
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• 2019

This study was analyzed about the relationship between culture microenvironment and cell differentiation of HPV 16 E6/E7-transfected immortalized oral keratinocyte(IHOK). By the alteration of culture environment, IHOK-EF and IHOK-EFKGM were obtained, and the modulation of cell properties was observed by cell proliferation assay, immunofluorescence, microarray, and quantitative real-time PCR analysis. IHOK-EF losed the properties of epithelial cells and obtained the properties of mesenchymal cells, and in the result of microarray analysis, genes related to the inhibition of differentiation such as IL6, TWIST1, and ID2 were highly expressed in IHOK-EF. When the culture environment was recovered to initial environment, these changes were recovered partially, presenting the return of genes involved in the inhibition of differentiation such as IL6, and ID2, particularly. This study will contribute to understand adjustment aspect for cell surviving according to the change of culture microenvironment in the study for determining the cell characteristic, and facilitate therapeutic approach for human disease by applying surviving study according to the change of cancer microenvironment.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.269-280
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• 2020

Time course gene expression data is a large amount of data observed over time in microarray experiments. This data can also simultaneously identify the level of gene expression. However, the experiment process is complex, resulting in frequent missing values due to various causes. In this paper, we propose a pattern consistency index adaptive nearest neighbors as a method of missing value imputation. This method combines the adaptive nearest neighbors (ANN) method that reflects local characteristics and the pattern consistency index that considers consistent degree for gene expression between observations over time points. We conducted a Monte Carlo simulation study to evaluate the usefulness of proposed the pattern consistency index adaptive nearest neighbors (PANN) method for two yeast time course data.