• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.78-78
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• 2006

Gene expression analysis can be performed by one-color (intensity-based) or two-color (ratio-based) microarray platforms depending on the specific applications and needs of the researcher. The traditional two-color approach is well founded from a historical and scientific standpoint, and the one-color approach, when paired with high quality microarrays and a robust workflow, offers additional flexibility in experimental design. Two of the major requirements of any microarray platform are system reproducibility, which provides the means for high confidence experiments and accurate comparison across multiple samples; and high sensitivity, for the detection of significant gene expression changes, including small fold changes across multiple gene sets. Each of these requirements is fulfilled by the Agilent One-color Gene Expression Platform as illustrated by the data included in this study. As a result, researchers have the ability to take advantage of the enhanced performance and sensitivity of Agilent's 60-mer oligonucleotide microarrays, and experience the first commercial microarray platform compatible with both one- and two-color detection.

• Journal of fish pathology
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• v.35 no.2
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• pp.141-155
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• 2022

Rock bream iridovirus (RBIV) causes high mortality and economic losses in the rock bream (Oplegnathus fasciatus) aquaculture industry in Korea. Viral open reading frames (ORFs) expression profiling at different RBIV infection stages was investigated using microarray approaches. Rock bream were exposed to the virus and held for 7 days at 23 ℃ before the water temperature was reduced to 17 ℃. Herein, 28% mortality was observed from 24 to 35 days post infection (dpi), after which no mortality was observed until 70 dpi (end of the experiment). A total of 27 ORFs were significantly up- or down-regulated after RBIV infection. In RBIV-infected rock bream, four viral genes were expressed after 2 dpi. Most RBIV ORFs (26 genes, 96.2%) were significantly elevated between 7 and 20 dpi. Among them, 12 ORF (44.4%) transcripts reached their peak expression intensity at 15 dpi, and 14 ORFs (51.8%) were at peak expression intensity at 20 dpi. Expression levels began to decrease after 25 dpi, and 92.6% of ORFs (25 genes) were expressed below 1-fold at 70 dpi. From the microarray data, in addition to the viral infection, viral gene expression profiles were categorized into three infection stages, namely, early (2 dpi), middle (7 to 20 dpi), and recovery (25 and 70 dpi). RBIV ORFs 009R, 023R, 032L, 049L, and 056L were remarkably expressed during RBIV infection. Furthermore, six ORFs (001L, 013R, 052L, 053L, 058L, and 061L) were significantly expressed only at 20 dpi. To verify the cDNA microarray data, we performed quantitative real-time PCR, and the results were similar to that of the microarray. Our results provide novel observations on broader RBIV gene expression at different stages of infection and the development of control strategies against RBIV infection.

• Genomics & Informatics
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• v.2 no.2
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• pp.92-98
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• 2004

A cDNA microarray experiment is one of the most useful high-throughput experiments in medical informatics for monitoring gene expression levels. Statistical analysis with a cDNA microarray medical data requires a normalization procedure to reduce the systematic errors that are impossible to control by the experimental conditions. Despite the variety of normalization methods, this. paper suggests a more general and synthetic normalization algorithm with a control gene set based on previous studies of normalization. Iterative normalization method was used to select and include a new control gene set among the whole genes iteratively at every step of the normalization calculation initiated with the housekeeping genes. The objective of this iterative normalization was to maintain the pattern of the original data and to keep the gene expression levels stable. Spatial plots, M&A (ratio and average values of the intensity) plots and box plots showed a convergence to zero of the mean across all genes graphically after applying our iterative normalization. The practicability of the algorithm was demonstrated by applying our method to the data for the human photo aging study.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• 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.617-626
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• 2009

A series of recent papers reported that the inter-gene correlations in Affymetrix microarray data sets were strong and long-ranged, and the assumption of independence or weak dependence among gene expression signals which was often employed without justification was in conflict with actual data. Qui et al. (2005) indicated that applying the nonparametric empirical Bayes method in which test statistics were pooled across genes for performing the statistical inference resulted in the large variance of the number of differentially expressed genes. Qui et al. (2005) attributed this effect to strong and long-ranged inter-gene correlations. Klebanov and Yakovlev (2007) demonstrated that the inter-gene correlations provided a rich source of information rather than being a nuisance in the statistical analysis and they developed, by transforming the original gene expression sequence, a sequence of independent random variables which they referred to as a ${\delta}$-sequence. We note in this report using two cDNA microarray data sets experimented in this country that the strong and long-ranged inter-gene correlations were still valid in cDNA microarray data and also the ${\delta}$-sequence of independence could be derived from the cDNA microarray data. This note suggests that the inter-gene correlations be considered in the future analysis of the cDNA microarray data sets.

• Journal of fish pathology
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• v.36 no.2
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• pp.191-211
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• 2023

Rock bream iridovirus (RBIV) causes high mortality and economic losses in rock bream (Oplegnathus fasciatus) aquaculture industry in Korea. Although, the immune responses of rock bream under RBIV infection have been studied, there is not much information at the different stages of infection (initial, middle and recovery). Gene expression profiling of rock bream under different RBIV infection stages was investigated using a microarray approaches. In total, 5699 and 6557 genes were significantly up- or down-regulated over 2-fold, respectively, upon RBIV infection. These genes were grouped into categories such as innate immune responses, adaptive immune responses, complements, lectin, antibacterial molecule, stress responses, DNA/RNA binding, energy metabolism, transport and cell cycle. Interestingly, hemoglobins (α and β) appears to be important during pathogenesis; it is highly up-regulated at the initial stage and is gradually decreased when the pathogen most likely multiplying and fish begin to die at the middle or later stage. Expression levels were re-elevated at the recovery stage of infection. Among up-regulated genes, interferon-related genes were found to be responsive in most stages of RBIV infection. Moreover, X-linked inhibitor of apoptosis (XIAP)-associated factor 1 (XAF1) expression was high, whereas expression of apoptosis-relate genes were low. In addition, stress responses were highly induced in the virus infection. The cDNA microarray data were validated using quantative real-time PCR. Our results provide novel inslights into the broad immune responses triggered by RBIV at different infection stages.

• 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.