• 한국자원식물학회지
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• 제21권3호
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• pp.210-215
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• 2008

Gene-expression analysis is increasingly important in biological research, with real-time reverse PCR (RTPCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. However, this technique requires important preliminary work for standardizing and optimizing the many parameters involved in the analysis. Plant stress studies are more and more based on gene expression. The analysis of gene expression requires sensitive and reproducible measurements for specific mRNA sequence. Several genes are regulated in response to abitoic stresses, such as salinity, and their gene products function in stress response and tolerance. The design of the primers and TaqMan probes for real-time PCR assays were carried out using the Primer $Express^{TM}$ software 3.0. The PCR efficiency was estimated through the linear regression of the dilution curve. To understand the expression pattern of various genes under salt stressed condition, we have developed a unique public resource of 9 stress-related genes in poplar. In this study, real-time RT-PCR was used to quantify the transcript level of 10 genes (9 stress-related genes and 1 house keeping gene) that could play a role in adaptation of Populus davidiana. Real-time RT-PCR analyses exhibited different expression ratios of related genes. The data obtained showed that determination of mRNA levels could constitute a new approach to study the stress response of P. davidiana after adaptation during growth in salinity condition.

• 한국생물물리학회:학술대회논문집
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• 한국생물물리학회 2003년도 정기총회 및 학술발표회
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• pp.77-77
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• 2003

Gene expression in a cell is regulated by mutual activations or repressions between genes. Identifying the gene regulation network will be one of the most important research topics in the post genomic era. We propose a linear dynamic model of gene regulation for the yeast cell cycle. A small gene network consisting of about 40 genes is reconstructed from the analysis of micro-array gene expression data of yeast S. cerevisiae published by P. Spellman et al. We show that the network construction is consistent with the result of the hierarchical cluster analysis.

• 응용통계연구
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• 제33권3호
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• pp.269-280
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• 2020

시간경로 유전자 발현 자료는 마이크로어레이 실험을 시간에 따라 관측한 대용량의 자료로 유전자 발현 수준을 동시에 파악할 수 있다. 하지만 실험 과정이 복잡하여 다양한 원인들에 의해 결측값이 자주 발생한다. 본 논문에서는 시간경로 유전자 발현 자료에 대한 결측값을 추정하는 방법으로 패턴 적응 최근접 이웃(pattern consistency index adaptive nearest neighbors; PANN) 방법을 제안하였다. 이 방법은 국소적 특징을 반영하는 적응 최근접 이웃(adaptive nearest neighbors; ANN) 방법과 관측 시점간 유전자 발현의 일치 정도를 고려하는 패턴일치지수를 결합시킨 것이다. 제안한 PANN 방법의 효능을 평가하기 위하여 두 가지의 실제 시간경로 자료들을 사용하여 몬테카를로 모의실험(Monte Carlo simulation study)을 시행하였다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2006년도 Principles and Practice of Microarray for Biomedical Researchers
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• pp.98-107
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• 2006

Chromosomal copy number changes (aneuploidies) are common in human populations. The extra chromosome can affect gene expression by whole-genome level. By gene expression microarray analysis, we want to find aberrant gene expression due to aneuploidies in Klinefelter (+X) and Down syndrome (+21). We have analyzed the inactivation status of X-linked genes in Klinefelter Syndrome (KS) by using X-linked cDNA microarray and cSNP analysis. We analyzed the expression of 190 X-linked genes by cDNA microarray from the lymphocytes of five KS patients and five females (XX) with normal males (XY) controls. cDNA microarray experiments and cSNP analysis showed the differentially expressed genes were similar between KS and XX cases. To analyze the differential gene expressions in Down Syndrome (DS), Amniotic Fluid (AF)cells were collected from 12 pregnancies at $16{\sim}18$ weeks of gestation in DS (n=6) and normal (n=6) subjects. We also analysis AF cells for a DNA microarray system and compared the chip data with two dimensional protein gel analysis of amniotic fluid. Our data may provide the basis for a more systematic identification of biological markers of fetal DS, thus leading to an improved understanding of pathogenesis for fetal DS.

• 생물정신의학
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• 제8권2호
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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.

• 한국작물학회지
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• 제55권4호
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• pp.371-378
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• 2010

For sensitive and accurate gene expression analysis, normalization of gene expression data against housekeeping genes is required. There are conventional housekeeping gene (e.g. ACT) that primarily function as an internal control of transcription. In this study, we performed an in silico analysis of 278 rice gene expression samples (GSM) in order to identify the gene that is most consistently expressed. Based on this analysis, we identified novel candidate housekeeping genes that displayed improved stability among the cross experimental conditions. Furthermore four of the most conventional housekeeping genes were included in our 30 other housekeeping genes among the most stable genes. Therefore, these 30 genes can he used to normalize transcription results in gene expression studies on rice at a broad range of experimental conditions.

• Asian Pacific Journal of Cancer Prevention
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• 제17권sup3호
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• pp.305-309
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• 2016

PTEN protein is an important tumour suppressor factor detectable by immunohistochemistry. The goal of the present study was to investigate the prognostic role of PTEN gene expression focusing on length of survival in breast cancer patients. This descriptive-analytical study was conducted on 100 breast cancer cases referred to Sabzevar hospitals in the north east of Iran between 2010 and 2011, followed up to 2015. The PTEN gene expression of tumour tissue samples was determined using specific monoclonal antibodies. The data were analyzed using Chi-square test and Fisher's exact test. Patient length of survival was analyzed after 4 years of follow-up using the Cox regression model. The PTEN gene was expressed in 70 of 100 samples, while being found at a high level in all noncancerous samples. There was an inverse significant relationship between expression of PTEN and tumour stage and grade (p<0.001). In addition, expression of PTEN in invasive ductal tumours was less than in non-invasive tumours. There was also an inverse significant relationship between the likelihood of death and PTEN gene expression (p<0.01). These findings indicate that lack of PTEN gene expression can be sign for a worse prognosis and poor survival in breast cancer.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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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.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• 대한약학회:학술대회논문집
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• 대한약학회 2003년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2-1
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• pp.60-61
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• 2003

Human Genome Project has recently been completed and the information on nucleotide sequences of our whole genome is now available at the public or commercial data banks. Next goals are to identify the functions of each gene and to elucidate the intracellular signal transduction pathways regulating gene expression. We have established a PCR-based bioassay to search for biologically active compounds that can modulate the expression of genes encoding important proteins. (omitted)