• 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.

• 한국약용작물학회:학술대회논문집
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• 2011.04a
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• pp.106-107
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• 2011

• Korean Journal of Biological Psychiatry
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• v.8 no.2
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• pp.203-207
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• 2001

The genome sequencing project has generated and will continue to generate enormous amounts of sequence data including 5 eukaryotic and about 60 prokaryotic genomes. Given this ever-increasing amounts of sequence information, new strategies are necessary to efficiently pursue the next phase of the genome project-the elucidation of gene expression patterns and gene product function on a whole genome scale. In order to assign functional information to the genome sequence, DNA chip(or gene microarray) technology was developed to efficiently identify the differential expression pattern of independent biological samples. DNA chip provides a new tool for genome expression analysis that may revolutionize many aspects of biotechnology including new drug discovery and disease diagnostics.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.139-147
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• 2021

Since whole-genome duplication (WGD) of diploid Chrysanthemum nankingense and de novo assembly whole-genome of C. seticuspe have been obtained, they have afforded to perceive the diversity evolution and gene discovery in the improved investigation of chrysanthemum breeding. The robust tools of high-throughput identification and analysis of gene function and expression produce their vast importance in chrysanthemum genomics. However, the gigantic genome size and heterozygosity are also mentioned as the major obstacles preventing the chrysanthemum breeding practices and functional genomics analysis. Nonetheless, some of technological contemporaries provide scientific efficient and promising solutions to diminish the drawbacks and investigate the high proficient methods for generous phenotyping data obtaining and system progress in future perspectives. This review provides valuable strategies for a broad overview about the high-throughput identification, and molecular analysis of gene function and expression in chrysanthemum. We also contribute the efficient proposition about specific protocols for considering chrysanthemum genes. In further perspective, the proper high-throughput identification will continue to advance rapidly and advertise the next generation in chrysanthemum breeding.

• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.52-57
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• 2007

The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.849-852
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• 2014

Reverse engineering of gene regulatory network is a challenging task in computational biology. To detect a regulatory relationship among genes from time series data is called reverse engineering. Reverse engineering helps to discover the architecture of the underlying gene regulatory network. Besides, it insights into the disease process, biological process and drug discovery. There are many statistical approaches available for reverse engineering of gene regulatory network. In our paper, we propose pairwise mutual information for the reverse engineering of a gene regulatory network from time series data. Firstly, we create random boolean networks by the well-known $Erd{\ddot{o}}s-R{\acute{e}}nyi$ model. Secondly, we generate artificial time series data from that network. Then, we calculate pairwise mutual information for predicting the network. We implement of our system on java platform. To visualize the random boolean network graphically we use cytoscape plugins 2.8.0.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.59-77
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• 2004

Since the introduction of DNA microarray, a revolutionary high through-put biological technology, a lot of papers have been published to deal with the analyses of the gene expression data from the microarray. In this paper we review most papers relevant to the cDNA microarray data, classify them in statistical methods' point of view, and present some statistical methods deserving consideration and future study.

• Molecules and Cells
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• v.46 no.1
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• pp.65-67
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• 2023

SMILES (simplified molecular-input line-entry system) information of small molecules parsed by one-hot array is passed to a convolutional neural network called black box. Outputs data representing a gene signature is then matched to the genetic signature of a disease to predict the appropriate small molecule. Efficacy of the predicted small molecules is examined by in vivo animal models. GSEA, gene set enrichment analysis.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2002.10a
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• pp.33-33
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• 2002