• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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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.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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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.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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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.

• Genomics & Informatics
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• v.2 no.1
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• pp.19-29
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• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

• Proceedings of the Korean Society of Embryo Transfer Conference
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• 2006.10a
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• pp.89-90
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• 2006

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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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.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4217-4224
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• 2014

Cholangiocarcinoma (CCA), a slow growing but highly metastatic tumor, is highly prevalent in Northeast Thailand. Specific tests that predict prognosis of CCA remain elusive. The present study was designed to investigate whether peripheral blood leukocyte (PBL) transcriptional profiles might be of use as a prognostic test in CCA patients. Gene expression profiles of PBLs from 9 CCA and 8 healthy subjects were conducted using the Affymetrix HG_U133 Plus 2.0 GeneChip. We indentified informative PBLs gene expression profiles that could reliably distinguish CCA patients from healthy subjects. Of these CCA specific genes, 117 genes were up regulated and 60 were down regulated. The molecular and cellular functions predicted for these CCA specific genes according to the Gene Ontology database indicated differential PBL expression of host immune response and tumor progression genes (EREG, TGF ${\beta}1$, CXCL2, CXCL3, IL-8, and VEGFA). The expression levels of 9 differentially expressed genes were verified in 36 CCA vs 20 healthy subjects. A set of three tumor invasion related genes (PLAU, CTSL and SERPINB2) computed as "prognostic index" was found to be an independent and statistically significant predictor for CCA patient survival. The present study shows that CCA PBLs may serve as disease predictive clinically accessible surrogates for indentifying expressed genes reflective of CCA disease severity.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.8
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• pp.372-377
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• 2008

Multiclass cancer classification has been actively investigated based on gene expression profiles, where it determines the type of cancer by analyzing the large amount of gene expression data collected by the DNA microarray technology. Since gene expression data include many genes not related to a target cancer, it is required to select informative genes in order to obtain highly accurate classification. Conventional rank-based gene selection methods often use ideal marker genes basically devised for binary classification, so it is difficult to directly apply them to multiclass classification. In this paper, we propose a novel method for multiclass gene selection, which does not use ideal marker genes but directly analyzes the distribution of gene expression. It measures the class-discriminability by discretizing gene expression levels into several regions and analyzing the frequency of training samples for each region, and then classifies samples by using the naive Bayes classifier. We have demonstrated the usefulness of the proposed method for various representative benchmark datasets of multiclass cancer classification.

• Molecular & Cellular Toxicology
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• v.3 no.4
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• pp.222-230
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• 2007

Some drugs may be limited in their clinical application due to their propensity towards their adverse effects. Toxicogenomic technology represents a useful approach for evaluating the toxic properties of new drug candidates early in the drug discovery process. Nitrofurantoin (NF) is clinical chemotherapeutic agent and antimicrobial and used to treatment of urinary tract infections. However, NF has been shown to result in pulmonary toxic effects. In this research, we revealed the changing expression gene profiles in BEAS-2B, human bronchial epithelial cell line, exposed to NF by using human oligonucleotide chip. Through the clustering analysis of gene expression profiles, we identified 136 up-regulated genes and 379 down-regulated genes changed by more than 2-fold by NF. This study identifies several interesting targets and functions in relation to NF-induced toxicity through a gene ontology analysis method including biological process, cellular components, molecular function and KEGG pathway.