• Journal of Life Science
• /
• v.17 no.7 s.87
• /
• pp.996-1001
• /
• 2007

To investigate whether sulindac, sulindac sulfone, and sulindac sulfide could affect cancer cell viabilities, human colorectal HCTl16 cells were treated with 10 ${\mu}M$ of each NSAID. Among treated NSAms, sulindac sulfide dramatically decreased the cell viabilities detected by MTS and the cytotoxic effect showed dose-dependent manner. To understand the molecular mechanism of cell death in response to sulindac sulfide treatment, we performed oligo DNA microarray analysis. We found that 23 genes were up-regulated more than 2 folds, whereas 33 genes were down-regulated more than 2 folds by treatment of 10 ${\mu}M$ sulindac sulfide. Among the up-regulated genes, we selected 3 genes (NAG-1, DDIT3, PCK2) and performed RT-PCR and quantitative real-time PCR to cofirm microarray data. The results of RT-PCR and real-time PCR were highly accorded with those of microarray experiment. As NAG-1 is well-known gene as tumor suppressor, we detected changes of NAG-1 expression by 10 ${\mu}M$ of sulindac, sulindac sulfone, and sulindac sulfide. The results of RT-PCR and quantitacve real-time PCR indicated that sulindac sulfide was the strongest inducer of NAG-1 among treated NSAIDS. This result implies that induction of NAG-1 by sulindac sulfide plays important role in cell death of colorectal cancer. Overall, we speculate that these results may be helpful in understanding the molecular mechanism of the cancer chemoprevention by sulindac sulfide in human colorectal cancer.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2000.11a
• /
• pp.41-44
• /
• 2000

With the advent of DNA microarray and "chip" technologies, gene expression in an organism can be monitored on a genomic scale, allowing the transcription levels of many genes to be measured simultaneously. Functional interpretation of massive expression data and linking such data to DNA sequences have become the new challenges to bioinformatics. I will us yeast cell cycle expression data analysis as an example to demonstrate how special database and computational methods may be used for extracting functional information, I will also briefly describe a novel clustering algorithm which has been applied to the cell cycle data.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.5
• /
• pp.2375-2382
• /
• 2016

Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.417-420
• /
• 2007

마이크로어레이는 생명과학 분야에서 사용되는 대규모의 유전자 발현정도를 동시에 측정할 수 있는 도구이다. 마이크로어레이 실험은 많은 양의 데이터를 생성하기 때문에, 자동화된 효과적인 분석기법이 필요하다. 이 논문에서는 약물의 영향 분석을 위해 약물의 투여량 및 투여후의 시간대별로 샘플을 추출하여, 마이크로어레이를 이용하여 유전자의 발현량을 분석하는 경우에, 약물에 대해서 반응하는 유전자를 추출하는 데이터 마이닝 기법을 제안한다. 제안한 방법에서는 유전자의 발현정도값을 이전 시간의 값을 기준값으로 하여 증가, 감소, 답보에 해당하는 기호로 매핑하여, 분석자가 원하는 패턴을 보이는 유전자를 추천한다. 한편, 유전자의 상호간에 많은 영향을 주고 받기 때문에 특정 약물을 투여할 때, 이에 직접적인 영향을 받는 것도 있지만, 이와는 전혀 상관없이 동작하는 것도 있기 때문에, 제안한 방법에서는 이러한 약물 투여와 유의성이 있을 가능성이 있는 유전자만을 전처리과정을 통해서 필터링하는 기법을 활용한다. 제안한 방법은 실제 약물 투여 실험 샘플에 대한 마이크로어레이 데이터에 적용하여 활용가능성을 확인하였다.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.2
• /
• pp.311-324
• /
• 2006

The Support Vector Machine(SVM) is very functional and efficient classification method to any other classification analysis method. However, its optimal extension to more than two classes is not obvious. In this paper several multi-category SVM methods are introduced and compared using simulation and real data sets. Also comparison with traditional multi-category classification and SVM based methods is performed.

• Communications for Statistical Applications and Methods
• /
• v.21 no.4
• /
• pp.349-361
• /
• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

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

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1067-1080
• /
• 2010

A number of methods have been developed to determine sample sizes in clinical trial, and most clinical trial organizations determine sample sizes based on the methods. In contrast, determining sufficient sample sizes needed for experiments using microarray chips is unsatisfactory and not widely in use. In this paper, our objective is to provide a guideline in determining sample sizes, utilizing reproducibility of real microarray data. In the reproducibility comparison, five methods for discovering differential expression are used: Fold change, Two-sample t-test, Wilcoxon rank-sum test, SAM, and LPE. In order to standardize gene expression values, both MAS5 and RMA methods are considered. According to the number of repetitions, the upper 20 and 100 gene accordances are also compared. In determining sample sizes, more realistic information can be added to the existing method because of our proposed approach.

• Development and Reproduction
• /
• v.18 no.1
• /
• pp.1-11
• /
• 2014

Early growth response 1 (Egr1) is a zinc-finger transcription factor to direct second-wave gene expression leading to cell growth, differentiation and/or apoptosis. While it is well-known that Egr1 controls transcription of an array of targets in various cell types, downstream target gene(s) whose transcription is regulated by Egr1 in the uterus has not been identified yet. Thus, we have tried to identify a list of potential target genes of Egr1 in the uterus by performing multi-step in silico promoter analyses. Analyses of mRNA microarray data provided a cohort of genes (102 genes) which were differentially expressed (DEGs) in the uterus between Egr1(+/+) and Egr1(-/-) mice. In mice, the frequency of putative EGR1 binding sites (EBS) in the promoter of DEGs is significantly higher than that of randomly selected non-DEGs, although it is not correlated with expression levels of DEGs. Furthermore, EBS are considerably enriched within -500 bp of DEG's promoters. Comparative analyses for EBS of DEGs with the promoters of other species provided power to distinguish DEGs with higher probability as EGR1 direct target genes. Eleven EBS in the promoters of 9 genes among analyzed DEGs are conserved between various species including human. In conclusion, this study provides evidence that analyses of mRNA expression profiles followed by two-step in silico analyses could provide a list of putative Egr1 direct target genes in the uterus where any known direct target genes are yet reported for further functional studies.

• The Korean Journal of Physiology and Pharmacology
• /
• v.10 no.5
• /
• pp.263-271
• /
• 2006

Since astrocytes were shown to play a central role in maintaining neuronal viability both under normal conditions and during stress such as ischemia, studies of the astrocytic response to stress are essential to understand many types of brain pathology. The micro array system permitted screening of large numbers of genes in biological or pathological processes. Therefore, the gene expression patterns in the in vitro model of astrocytes following exposure to oxygen-glucose deprivation (OGD) were evaluated by using the micro array analysis. Primary astrocytic cultures were prepared from postnatal Swiss Webster mice. The cells were exposed to OGD for 4 hrs at $37^{\circ}C$ prior to cell harvesting. From the cultured cells, we isolated mRNA, synthesized cDNA, converted to biotinylated cRNA and then reacted with GeneChips. The data were normalized and analyzed using dChip and GenMAPP tools. After 4 hrs exposure to OGD, 4 genes were increased more than 2 folds and 51 genes were decreased more than 2 folds compared with the control condition. The data suggest that the OGD has general suppressive effect on the gene expression with the exception of some genes which are related with ischemic cell death directly or indirectly. These genes are mainly involved in apoptotic and protein translation pathways and gap junction component. These results suggest that microarray analysis of gene expression may be useful for screening novel molecular mediators of astrocyte response to ischemic injury and making profound understanding of the cellular mechanisms as a whole. Such a screening technique should provide insights into the molecular basis of brain disorders and help to identify potential targets for therapy.