• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.17-25
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• 2005

There are some critical drawbacks in the use of biomarkers for a global assessment of the toxicological impacts many chemicals and environmental pollutants have, primarily due to an individual biomarker's specificity for an explicit chemical or toxicant. In other words, the biomarker-based assessment methodology used to analyze toxicological effects lacks a high-throughput capability. Therefore, eco-toxicogenomics, or the study of toxicogenomics with organisms present within a given environmental locale, has recently been introduced with the advent of the so-called "-omics" era, which began with the creation of microarray technologies. Fish are comparable with humans in their toxicological responses and thus data from toxicogenomic studies performed with fish could be applied, with appropriate tools and implementation protocols, to the evaluation of environments where human or animal health is of concern. At present, there have been very active research streams for developing expression sequence tag (EST) databases (DBs) for zebra fish and rainbow trout. Even though few reports involve toxicogenomic studies with fish, a few groups have successfully fabricated and used cDNA microarrays or oligo DNA chips when studying the toxicological impacts of hypoxia or some toxicants with fish. Furthermore, it is strongly believed that this technology can also be implemented with non-model fish. With the standardization of DNA microarray technologies and ample progress in bioinformatics and proteomic technologies, data obtained from DNA microarray technologies offer not only multiple biomarker assays or an analysis of gene expression profiles, but also a means of elucidating gene networking, gene-gene relations, chemical-gene interactions, and chemical-chemical relationships. Accordingly, the ultimate target of eco-toxicogenomics should be to predict and map the pathways of stress propagation within an organism and to analyze stress networking.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.107-116
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• 2004

The advent of microarray technologies gives an opportunity to moni tor the expression of ten thousands of genes, simultaneously. Such microarray data can be deteriorated by experimental errors and image artifacts, which generate non-negligible outliers that are estimated by 15% of typical microarray data. Thus, it is an important issue to detect and correct the se faulty probes prior to high-level data analysis such as classification or clustering. In this paper, we propose a systematic procedure for the detection of faulty probes and its proper correction in Genechip array based on multivariate statistical approaches. Principal component analysis (PCA), one of the most widely used multivariate statistical approaches, has been applied to construct a statistical correlation model with 20 pairs of probes for each gene. And, the faulty probes are identified by inspecting the squared prediction error (SPE) of each probe from the PCA model. Then, the outlying probes are reconstructed by the iterative optimization approach minimizing SPE. We used the public data presented from the gene chip project of human fibroblast cell. Through the application study, the proposed approach showed good performance for probe correction without removing faulty probes, which may be desirable in the viewpoint of the maximum use of data information.

• Journal of Environmental Science International
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• v.24 no.5
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• pp.623-631
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• 2015

Microarray technology provides a unique tool for the determination of gene expression at the level of messenger RNA (mRNA). This study, the mRNA expression profiles provide insight into the mechanism of action of cadmium in Fleshy shrimp (Fenneropenaeus chinensis). The ability of genomic technologies was contributed decisively to development of new molecular biomarkers and to the determination of new possible gene targets. Also, it can be approach for monitoring of trace metal using oligo-chip microarray-based in potential model marine user level organisms. 15K oligo-chip for F. chinensis that include mostly unique sets of genes from cDNA sequences was developed. A total of 13,971 spots (1,181 mRNAs up- regulated and 996 down regulated) were identified to be significantly expressed on microarray by hierarchical clustering of genes after exposure to cadmium for different conditions (Cd24-5000 and Cd48-1000). Most of the changes of mRNA expression were observed at the long time and low concentration exposure of Cd48-1000. But, gene ontology analysis (GO annotation) were no significant different between experiments groups. It was observed that mRNA expression of main genes involved in metabolism, cell component, molecular binding and catalytic function. It was suggested that cadmium inhibited metabolism and growth of F. chinensis.

• BMB Reports
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• v.37 no.1
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• pp.1-10
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• 2004

DNA and RNA quantifications are widely used in biological and biomedical research. In the last ten years, many technologies have been developed to enable automated and high-throughput analyses. In this review, we first give a brief overview of how DNA and RNA quantifications are carried out. Then, five technologies (microarrays, SAGE, differential display, real time PCR and real competitive PCR) are introduced, with an emphasis on how these technologies can be applied and what their limitations are. The technologies are also evaluated in terms of a few key aspects of nucleic acids quantification such as accuracy, sensitivity, specificity, cost and throughput.

• BMB Reports
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• v.38 no.5
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• pp.517-525
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• 2005

Solid phase peptide synthesis method, which was introduced by Merrifield in 1963, has spawned the concept of combinatorial chemistry. In this review, we summarize the present technologies of solid phase peptide synthesis (SPPS) that are related to combinatorial chemistry. The conventional methods of peptide library synthesis on polymer support are parallel synthesis, split and mix synthesis and reagent mixture synthesis. Combining surface chemistry with the recent technology of microelectronic semiconductor fabrication system, the peptide microarray synthesis methods on a planar solid support are developed, which leads to spatially addressable peptide library. There are two kinds of peptide microarray synthesis methodologies: pre-synthesized peptide immobilization onto a glass or membrane substrate and in situ peptide synthesis by a photolithography or the SPOT method. This review also discusses the application of peptide libraries for high-throughput bioassays, for example, peptide ligand screening for antibody or cell signaling, enzyme substrate and inhibitor screening as well as other applications.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.7
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• pp.328-334
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• 2002

The DNA chips are devices associating the specific recognition properties of two DNA single strands through hybridization process with the performances of the microtechnology. In the literature, the "Gene chip" or "DNA chip" terminology is employed in a wide way and includes macroarrays and microarrays. Standard definitions are not yet clearly exposed. Generally, the difference between macro and microarray concerns the number of active areas and their size, Macroarrays correspond to devices containing some tens spots of 500$\mu$m or larger in diameter. microarrays concern devices containing thousnads spots of size less than 500$\mu$m. The key technical parameters for evaluating microarray-manufacturing technologies include microarray density and design, biochemical composition and versatility, repreducibility, throughput, quality, cost and ease of prototyping. Here we report, a new method in which minute particles are arranged in a random fashion on a chip pattern using random fluidic self-assembly (RFSA) method by hydrophobic interaction. We intend to improve the stability of the particles at the time of arrangement by establishing a wall on the chip pattern, besides distinction of an individual particle is enabled by giving a tag structure. This study demonstrates the fabrication of a chip pattern, immobilization of DNA to the particles and arrangement of the minute particle groups on the chip pattern by hydrophobic interaction.ophobic interaction.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.335-348
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• 2005

The rapid development of microarray technologies enabled the monitoring of expression levels of thousands of genes simultaneously. Recently, the time course gene expression data are often measured to study dynamic biological systems and gene regulatory networks. For the data, biologists are attempting to group genes based on the temporal pattern of their expression levels. We apply the consensus clustering algorithm to a time course gene expression data in order to infer statistically meaningful information from the measurements. We evaluate each of consensus clustering and existing clustering methods with various validation measures. In this paper, we consider hierarchical clustering and Diana of existing methods, and consensus clustering with hierarchical clustering, Diana and mixed hierachical and Diana methods and evaluate their performances on a real micro array data set and two simulated data sets.

• Journal of Preventive Medicine and Public Health
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• v.35 no.2
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• pp.83-91
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• 2002

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational sciences. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolutions both in bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever much the same way that biochemistry did a generation ago. The paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization, primary pattern analysis, and machine learning algorithms will be presented. Use of integrated biochip informatics technologies, text mining of factual and literature databases, and integrated management of biomolecular databases will be discussed. Each step will be given with real examples in the context of clinical relevance. Issues of linking molecular genotype and clinical phenotype information will be discussed.

• Journal of Plant Biotechnology
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• v.37 no.2
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• pp.115-124
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• 2010

As the completion of genome sequencing, large collection of expression data and the great efforts in annotating plant genomes, the next challenge is to systematically assign functions to all predicted genes in the genome. Functional genome analysis of plants has entered the high-throughput stage. The generations and collections of mutants at the genome-wide level form technological platform of functional genomics. However, to identify the exact function of unknown genes it is necessary to understand each gene's role in the complex orchestration of all gene activities in the plant cell. Gene function analysis therefore necessitates the analysis of temporal and spatial gene expression patterns. The most conclusive information about changes in gene expression levels can be gained from analysis of the varying qualitative and quantitative changes of messenger RNAs, proteins and metabolites. New technologies have been developed to allow fast and highly parallel measurements of these constituents of the cell that make up gene activity. We have reviewed currently employed technologies to identify unknown functions of predicted genes including map-based cloning, insertional mutagenesis, reverse genetics, chemical mutagenesis, microarray analysis, FOX-hunting system, gene silencing mutagenesis, proteomics and chemical genomics. Recent improvements in technologies for functional genomics enable whole-genome functional analysis, and thus open new avenues for studies of the regulations and functions of unknown genes in plants.