• Toxicological Research
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• v.22 no.4
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• pp.323-328
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• 2006

Global gene expression profile was analyzed by microarray analysis of rat liver RNA after acute acetaminophen (APAP) administration. A single dose of 1g/kg body weight of APAP was given orally, and the liver samples were obtained after 24, 48 h, and 2 weeks. Histopathologic and biochemical studies enabled the classification of the APAP effect into injury (24 and 48 h) and regeneration (2 weeks) stages. The expression levels of 4900 clones on a custom rat gene microarray were analyzed and 484 clones were differentially expressed with more than a 1.625-fold difference(which equals 0.7 in log2 scale) at one or more time points. Two hundred ninety seven clones were classified as injury-specific clones, while 149 clones as regeneration-specific ones. Characteristic gene expression profiles could be associated with APAP-induced gene expression changes in lipid metabolism, stress response, and protein metabolism. We established a global gene expression profile utilizing microarray analysis in rat liver upon acute APAP administration with a full chronological profile that not only covers injury stage but also later point of regeneration stage.

• BMB Reports
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• v.44 no.5
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• pp.291-297
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• 2011

Aging is one of the most complicated biological processes in all species. A number of different model organisms from yeast to monkeys have been studied to understand the aging process. Until recently, many different age-related genes and age-regulating cellular pathways, such as insulin/IGF-1-like signal, mitochondrial dysfunction, Sir2 pathway, have been identified through classical genetic studies. Parallel to genetic approaches, genome-wide approaches have provided valuable insights for the understanding of molecular mechanisms occurring during aging. Gene expression profiling analysis can measure the transcriptional alteration of multiple genes in a genome simultaneously and is widely used to elucidate the mechanisms of complex biological pathways. Here, current global gene expression profiling studies on normal aging and age-related genetic/environmental interventions in widely-used model organisms are briefly reviewed.

• Mycobiology
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• v.50 no.5
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• pp.366-373
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• 2022

Regulation of proper gene expression is important for cellular and organismal survival, maintenance, and growth. Abnormal gene expression, even for a single critical gene, can thwart cellular integrity and normal physiology to cause diseases, aging, and death. Therefore, gene expression profiling serves as a powerful tool to understand the pathology of diseases and to cure them. In this study, the difference in gene expression in Flammulina velutipes was compared between the wild type (WT) mushroom and the mutant one with clogging phenomenon. Differentially expressed transcripts were screened to identify the candidate genes responsible for the mutant phenotype using the DNA microarray analysis. A total of 88 genes including 60 upregulated and 28 downregulated genes were validated using the real-time quantitative PCR analysis. In addition, proteomic differences between the WT and mutant mushroom were analyzed using two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Interestingly, the genes identified by these genomic and proteomic analyses were involved in stress response, translation, and energy/sugar metabolism, including HSP70, elongation factor 2, and pyruvate kinase. Together, our data suggest that the aberrant expression of these genes attributes to the mutant clogging phenotype. We propose that these genes can be targeted to foster normal growth in F. velutipes.

• Journal of Plant Biotechnology
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• v.37 no.2
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• pp.144-152
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• 2010

As the International Rice Genome Sequencing Project (IRGSP) was completed in 2005 and opened to the public, many countries are making a lot of investments in researches on the utilization of sequence information along with system development. Also, the necessity of the functional genomics researches using microarray is increased currently to secure unique genes related with major agricultural traits and analyze metabolic pathways. Microrarray enables efficient analysis of large scale gene expression and related transcription regulation. This review aims to introduce available microarrays made based on rice genome information and current status of gene expression analysis using these microarrays integrated with the databases available to the public. Also, we introduce the researches on the large scale functional analysis of genes related with useful traits and genetic networks. Understanding of the mechanism related with mutual interaction between proteins with co-expression among rice genes can be utilized in the researches for improving major agricultural traits. The direct and indirect interactions of various genes would provide new functionality of rice. The recent results of the various expression profiling analysis in rice will promote functional genomic researches in plants including rice and provide the scientists involved in applications researches with wide variety of expression informations.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Proceedings of the Zoological Society Korea Conference
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• 2003.08a
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• pp.56-56
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• 2003

No Abstract, See Full Text

• The Korea Genome Conference
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• 2005.09a
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• pp.68-68
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• 2005

• The Korea Genome Conference
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• 2005.09a
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• pp.93-93
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• 2005

• Genomics & Informatics
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• v.15 no.1
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• pp.28-37
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• 2017

Obesity is a highly prevalent, chronic disorder that has been increasing in incidence in young patients. Both epigenetic and genetic aberrations may play a role in the pathogenesis of obesity. Therefore, in-depth epigenomic and genomic analyses will advance our understanding of the detailed molecular mechanisms underlying obesity and aid in the selection of potential biomarkers for obesity in youth. Here, we performed microarray-based DNA methylation and gene expression profiling of peripheral white blood cells obtained from six young, obese individuals and six healthy controls. We observed that the hierarchical clustering of DNA methylation, but not gene expression, clearly segregates the obese individuals from the controls, suggesting that the metabolic disturbance that occurs as a result of obesity at a young age may affect the DNA methylation of peripheral blood cells without accompanying transcriptional changes. To examine the genome-wide differences in the DNA methylation profiles of young obese and control individuals, we identified differentially methylated CpG sites and investigated their genomic and epigenomic contexts. The aberrant DNA methylation patterns in obese individuals can be summarized as relative gains and losses of DNA methylation in gene promoters and gene bodies, respectively. We also observed that the CpG islands of obese individuals are more susceptible to DNA methylation compared to controls. Our pilot study suggests that the genome-wide aberrant DNA methylation patterns of obese individuals may advance not only our understanding of the epigenomic pathogenesis but also early screening of obesity in youth.

• Genomics & Informatics
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• v.17 no.1
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• pp.8.1-8.10
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• 2019

Alveolar type II cells constitute a small fraction of the total lung cell mass. However, they play an important role in many cellular processes including trans-differentiation into type I cells as well as repair of lung injury in response to toxic chemicals and respiratory pathogens. Transcription factors are the regulatory proteins dynamically modulating DNA structure and gene expression. Transcription factor profiling in microarray datasets revealed that several members of AP1, ATF, $NF-{\kappa}B$, and C/EBP families involved in diverse responses were expressed in mouse lung type II cells. A transcriptional factor signature consisting of Cebpa, Srebf1, Stat3, Klf5, and Elf3 was identified in lung type II cells, Sox9+ pluripotent lung stem cells as well as in mouse lung development. Identification of the transcription factor profile in mouse lung type II cells will serve as a useful resource and facilitate the integrated analysis of signal transduction pathways and specific gene targets in a variety of physiological conditions.