• 제어로봇시스템학회논문지
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• 제11권10호
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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.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 춘계 학술발표회 논문집
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• pp.85-90
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• 2002

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time we are interested in testing gene expression profiles for different experimental groups. We propose a statistical test based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Using this test, we can detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.63-68
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• 2005

Transcription factors regulate gene expression by binding to gene upstream region. Each transcription factor has the specific binding site in promoter region. So the analysis of gene upstream sequence is necessary for understanding regulatory mechanism of genes, under a plausible idea that assumption that DNA sequence motif profiles are closely related to gene expression behaviors of the corresponding genes. Here, we present an effective approach to the analysis of the relation between gene expression profiles and gene upstream sequences on the basis of kernel canonical correlation analysis (kernel CCA). Kernel CCA is a useful method for finding relationships underlying between two different data sets. In the application to a yeast cell cycle data set, it is shown that gene upstream sequence profile is closely related to gene expression patterns in terms of canonical correlation scores. By the further analysis of the contributing values or weights of sequence motifs in the construction of a pair of sequence motif profiles and expression profiles, we show that the proposed method can identify significant DNA sequence motifs involved with some specific gene expression patterns, including some well known motifs and those putative, in the process of the yeast cell cycle.

• Asian-Australasian Journal of Animal Sciences
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• 제25권10호
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• pp.1481-1492
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• 2012

The interaction of the genes involved in intestinal development is the molecular basis of the regulatory mechanisms of intestinal development. The objective of this study was to identify the significant pathways and key genes that regulate intestinal development in Landrace piglets, and elucidate their rules of operation. The differential expression of genes related to intestinal development during suckling time was investigated using a porcine genome array. Time sequence profiles were analyzed for the differentially expressed genes to obtain significant expression profiles. Subsequently, the most significant profiles were assayed using Gene Ontology categories, pathway analysis, network analysis, and analysis of gene co-expression to unveil the main biological processes, the significant pathways, and the effective genes, respectively. In addition, quantitative real-time PCR was carried out to verify the reliability of the results of the analysis of the array. The results showed that more than 8000 differential expression transcripts were identified using microarray technology. Among the 30 significant obtained model profiles, profiles 66 and 13 were the most significant. Analysis of profiles 66 and 13 indicated that they were mainly involved in immunity, metabolism, and cell division or proliferation. Among the most effective genes in these two profiles, CN161469, which is similar to methylcrotonoyl-Coenzyme A carboxylase 2 (beta), and U89949.1, which encodes a folate binding protein, had a crucial influence on the co-expression network.

• Asian Pacific Journal of Cancer Prevention
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• 제17권5호
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• pp.2519-2525
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• 2016

There are several existing reports of microarray chip use for assessment of altered gene expression in different diseases. In fact, there have been over 1.5 million assays of this kind performed over the last twenty years, which have influenced clinical and translational research studies. The most commonly used DNA microarray platforms are Affymetrix GeneChip and Quality Control Software along with their GeneChip Probe Arrays. These chips are created using several quality controls to confirm the success of each assay, but their actual impact on gene expression profiles had not been previously analyzed until the appearance of several bioinformatics tools for this purpose. We here performed a data mining analysis, in this case specifically focused on ovarian cancer, as well as healthy ovarian tissue and ovarian cell lines, in order to confirm quality control results and associated variation in gene expression profiles. The microarray data used in our research were downloaded from ArrayExpress and Gene Expression Omnibus (GEO) and analyzed with Expression Console Software using RMA, MAS5 and Plier algorithms. The gene expression profiles were obtained using Partek Genomics Suite v6.6 and data were visualized using principal component analysis, heat map, and Venn diagrams. Microarray quality control analysis showed that roughly 40% of the microarray files were false negative, demonstrating over- and under-estimation of expressed genes. Additionally, we confirmed the results performing second analysis using independent samples. About 70% of the significant expressed genes were correlated in both analyses. These results demonstrate the importance of appropriate microarray processing to obtain a reliable gene expression profile.

• Molecules and Cells
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• 제20권1호
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• pp.97-104
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• 2005

To investigate apoptosis in HC11 mammary epithelial cells, we compared the gene expression profiles of actively growing and serum-starved apoptotic cells using a mouse apoptosis gene array and $^{33}P$-labeled cDNA prepared from the RNA of the two cultures. Analysis of the arrays showed that expression of several genes such as clusterin, secreted frizzled related protein mRNA (sFRP-1), CREB-binding protein (CBP), and others was higher in the apoptotic cells whereas expression of certain genes including survivin, cell division cycle 2 homolog A (CDC2), and cyclin A was lower. These expression patterns were confirmed by RT-PCR and/or Northern analyses. We compared the expression of some of these genes in the mouse mammary gland under various physiological conditions. The expression levels of genes (clusterin, CBP, and M6P-R) up-regulated in apoptotic conditions were higher at involution than during lactation. On the other hand, genes (Pin, CDC2) downregulated in apoptotic conditions were relatively highly expressed in virgin and pregnant mice. We conclude that certain genes such as clusterin, sFRP-1, GAS1 and CBP are induced in apoptotic mammary epithelial cells, and others are repressed. Moreover, the apoptosis array is an efficient technique for comparing gene expression profiles in different states of the same cell type.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2001년도 제2회 생물정보 워크샵 (DNA Chip Bioinformatics)
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• pp.97-115
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• 2001

cDNA microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. Many statistical analysis tools become widely applicable to the analysis of cDNA microarray data. In this talk, we consider a two-way ANOVA model to differentiate genes that have high variability and ones that do not. Using this model, we detect genes that have different gene expression profiles among experimental groups. The two-way ANOVA model is illustrated using cDNA microarrays of 3,800 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.

• 대한의생명과학회지
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• 제18권3호
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• pp.313-318
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• 2012

Acanthamoeba is an opportunistic pathogen known to cause granulomatous amoebic encephalitis and amebic keratitis. Acanthamoeba exhibits life cycle consisting of trophozoite and cyst, and the cyst is highly resistant to variable antibiotics and therapeutic agents. To understand the encystation mechanism of Acanthamoeba, the expression profiles of trophozoite and cyst were compared by gene ontology (GO) analysis. Ribosomal proteins and cytoskeletal proteins were highly expressed in trophozoite. In cyst, various protease, and signal transduction - and protein turnover - related proteins were highly expressed. These results correlated with eukaryotic orthologous groups (KOG) assignment and microarray analysis of Acanthamoeba trophozoite and cyst ESTs. The information of differential expression profiles of trophozoite and cyst would provide important clues for research on encystation mechanism of cyst forming protozoa including Acanthamoeba.

• 한국수정란이식학회:학술대회논문집
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• 한국수정란이식학회 2006년도 The 6th Intermational Symposium on Developmental Biotechnology
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• pp.89-90
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• 2006

• Asian Pacific Journal of Cancer Prevention
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• 제13권8호
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• pp.3605-3609
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• 2012

Lung cancer seriously threatens human health, so it is important to investigate gene expression changes in affected individuals in comparison with healthy people. Here we compared the gene expression profiles between smokers with and without lung cancer. We found that the majority of the expressed genes (threshold was set as 0.1 RPKM) were the same in the two samples, with a small portion of the remainder being unique to smokers with and without lung cancer. Expression distribution patterns showed that most of the genes in smokers with and without lung cancer are expressed at low or moderate levels. We also found that the expression levels of the genes in smokers with lung cancer were lower than in smokers without lung cancer in general. Then we detected 27 differentially expressed genes in smokers with versus without lung cancer, and these differentially expressed genes were foudn to be involved in diverse processes. Our study provided detail expression profiles and expression changes between smokers with and without lung cancer.