• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.1731-1735
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• 2013

Objective: We aimed to identify key genes, pathways and function modules in the development of diffuse large B-cell lymphoma (DLBCL) with microarray data and interaction network analysis. Methods: Microarray data sets for 7 DLBCL samples and 7 normal controls was downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified with Student's t-test. KEGG functional enrichment analysis was performed to uncover their biological functions. Three global networks were established for immune system, signaling molecules and interactions and cancer genes. The DEGs were compared with the networks to observe their distributions and determine important key genes, pathways and modules. Results: A total of 945 DEGs were obtained, 272 up-regulated and 673 down-regulated. KEGG analysis revealed that two groups of pathways were significantly enriched: immune function and signaling molecules and interactions. Following interaction network analysis further confirmed the association of DEGs in immune system, signaling molecules and interactions and cancer genes. Conclusions: Our study could systemically characterize gene expression changes in DLBCL with microarray technology. A range of key genes, pathways and function modules were revealed. Utility in diagnosis and treatment may be expected with further focused research.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
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• pp.259-261
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• 2005

수천개의 Gene Expression Measurement를 생성해 내는 DNA Microarray 연구는 조직과 세포의 표본으로부터 진단에 유용한 Gene Expression 정보를 모으게 된다. 이런 종류의 Data를 분석하기 위하여 SVM(Support Vector Machine)을 사용한 새로운 방법이 연구되어왔다. 본 논문에서는 Gene Expression Data에 대한 고유벡터(Eigen Vector)를 이용하여 SVM의 성능을 향상시키고 질병진단에 유용한 Gene을 찾아 내는 알고리즘을 기술한다. 고유벡터를 통하여 Gene을 선택적으로 SVM Learning에 참가 시키고 분류의 결과를 통하여 추가된 Gene이 질병 진단에 미치는 영향력을 알아냄으로써 질병에 대한 Gene 역할을 파악 하는데 활용할 수 있다.

• Genomics & Informatics
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• 제1권1호
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• pp.20-24
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• 2003

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.

• Molecules and Cells
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• 제25권2호
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• pp.272-278
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• 2008

The carbon-ion beam (CIB) generated by the heavy-ion medical accelerator in Chiba (HIMAC) was targeted to 7-day-old rice. Physiological parameters such as growth, and gene expression profiles were examined immediately after CIB irradiation. Dose-dependent growth suppression was seen three days post-irradiation (PI), and all the irradiated plants died by 15 days PI. Microarray (Agilent rice 22K) analysis of the plants immediately after irradiation (iai) revealed effects on gene expression at 270 Gy; 353 genes were up-regulated and 87 down-regulated. Exactly the same set of genes was affected at 90 Gy. Among the highly induced genes were genes involved in information storage and processing, cellular processes and signaling, and metabolism. RT-PCR analysis confirmed the microarray data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.623-626
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• 2006

Group of genes controls the functioning of a cell by complex interactions. These interacting gene groups are called Gene Regulatory Networks (GRNs). Two previous data mining approaches, clustering and classification have been used to analyze gene expression data. While these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rule. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and detect gene expression patterns applying FP-growth algorithm. And then, we construct gene regulatory network from frequent gene patterns using chain rule. Finally, we validated our proposed method by showing that our experimental results are consistent with published results.

• 한국미생물·생명공학회지
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• 제38권1호
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• pp.70-76
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• 2010

The single-stranded large circular (LC)-sense DNA were utilized as probes for DNA chip experiments. The microarray experiment using LC-sense DNA probes found differentially expressed genes in A549 cells as compared to WI38VA13 cells, and microarray data were well-correlated with data acquired from quantitative real-time RT-PCR. A 5K LC-sense DNA microarray was prepared, and the repeated experiments and dye swap test showed consistent expression patterns. Subsequent functional analysis using LC-antisense library of overexpressed genes identified several genes involved in A549 cell growth. These experiments demonstrated proper feature of LC-sense molecules as probe DNA for microarray and the potential utility of the combination of LC-sense microarray and antisense libraries for an effective functional validation of genes.

• Bulletin of the Korean Chemical Society
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• 제30권2호
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• pp.323-326
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• 2009

SR proteins are essential splicing regulators and also modulate alternative splicing events, which function both as redundant and substrate-specific manner. The Drosophila B52/SRp55, a member of the SR protein family, is essential for the fly development in vivo, as deletion of B52 gene results in lethality of animals at the second instar larval stage. Identification of the splicing target genes of B52 thus should be crucial for the understanding of the specific developmental role of B52 in vivo. In this study, we performed whole-genome DNA microarray experiments with a B52- knock-out animal. Analysis of the microarray data not only provided the B52-dependent gene expression signature, but also revealed a larval-stage specific, alternative splicing target gene of B52. Our result thus provides a starting point to understand the essential function of B52 at the organismal level.

• 전자공학회논문지CI
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• 제42권1호
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• pp.1-7
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• 2005

마이크로어레이 데이터의 클러스터 분석은 생물학적으로 연관성 있는 유전자 그룹을 찾기 위해 종종 사용되는 방법이다. 기능적으로 연관된 유전자들이 대개 유사한 발현 패턴을 나타내는 특징을 이용하여 유사한 발현 프로파일을 가진 유전자 그룹을 찾아냄으로써 알려지지 않은 유전자들의 기능을 같은 그룹에 속한 다른 유전자로부터 유추할 수 있기 때문이다. 본 논문에서는 클러스터 분석을 위해 시드 클러스터링 알고리즘을 새로이 제안하고, 이 방법을 마이크로어레이 데이터 분석에 적용해본다. 시드 클러스터링 방법은 주어진 데이터를 계산적으로 분석하여 시드 패턴을 자동 추출하고, 이러한 시드 패턴을 목적 클러스터의 프로토타입 벡터로서 간주하여 클러스터를 생성하는 방법이다. 이러한 시드 클러스터링 방법은 수학적 원리에 기초하고 있기 때문에, 매우 체계적인 방법으로 안정적이며 일관성 있는 클러스터링 결과를 생성할 수 있다. 또한, 실제 마이크로어레이 데이터 분석에 적용해본 결과 데이터에 내재된 각 클러스터를 대표하는 시드 패턴을 매우 효과적으로 자동 추출할 수 있었으며, 클러스터링 결과 또한 타 방법에 비해 다소 우월한 경향을 나타내었다.

• Journal of Ginseng Research
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• 제40권1호
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• pp.1-8
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• 2016

Background: Korean Red Ginseng (KRG) is a herbal medicine used in Asian countries and is very popular for its beneficial biological properties. Diabetes mellitus (DM) and its complications are rapidly becoming a global public health concern. The literature on transcriptional changes induced by KRG in rat models of diabetic retinopathy is limited. Considering these facts, we designed this study to determine whether retinopathy-associated genes are altered in retinas of rats with DM and whether the induced changes are reversed by KRG. Methods: Male Sprague-Dawley rats were intravenously injected with streptozotocin (50 mg/kg body weight) to induce DM, following which, KRG powder (200 mg/kg body weight) was orally administered to the KRG-treated DM rat group for 10 wks. The rats were then sacrificed, and their retinas were harvested for total RNA extraction. Microarray gene expression profiling was performed on the extracted RNA samples. Results: From among > 31,000 genes investigated, the expression of 268 genes was observed to be upregulated and that of 58 genes was downregulated, with twofold altered expression levels in the DM group compared with those in the control group. Moreover, 39 genes were upregulated more than twofold and 84 genes were downregulated in the KRG-treated group compared to the DM group. The expression of the genes was significantly reversed by KRG treatment; some of these genes were analyzed further to verify the results of the microarray experiments. Conclusion: Taken together, our data suggest that reversed changes in the gene expression may mediate alleviating activities of KRG in rats with diabetic retinopathy.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.41-44
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• 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.