• 제목/요약/키워드: Microarray Data

검색결과 473건 처리시간 0.029초

Effect of Korean Red Ginseng treatment on the gene expression profile of diabetic rat retina

  • Yang, Hana;Son, Gun Woo;Park, Hye Rim;Lee, Seung Eun;Park, Yong Seek
    • 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.

Gene Expression Profiling in C57BL/6 Mice Treated with the Anorectic Drugs Sibutramine and Phendimetrazine and Their Mechanistic Implications

  • Ko, Moon-Jeong;Choi, Hyo-Sung;Ahn, Joon-Ik;Kim, So-Young;Jeong, Ho-Sang;Chung, Hye-Joo
    • Genomics & Informatics
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    • 제6권3호
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    • pp.117-125
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    • 2008
  • Recently, obesity has become a worldwide public health concern and the use of anorectic drugs has drastically increased. In this study, sibutramine and phendimetrazine, representative marketed anorectics, were repeatedly administered per os on a daily basis into C57BL/6 mice and the effects of these drugs on food intakes, body weight changes and gene expression profiles were monitored for up to following 7 days. Methamphetamine, which has a potent anorectic effect, was used as a positive control. Anorectic effects were sustained only for two days by phendimetrazine or methamphetamine, but for six days by sibutramine. The modulations of gene expressions in the hypothalamus and the striatum were investigated using microarrays on day 2 and day 7 post-administration, which corresponded to the anorectic period and a return of appetite respectively, for all three drugs tested. Differences in overall gene expression profiles in the stratum on day 2 for sibutramine and phendimetrazine seems to reflect difference between the two in terms of the onsets of drug tolerance. According to microarray findings, the Ankrd26 gene appears to have an important anorectic role, whereas the up-regulation of the olfaction system appeared to be involved in the drug tolerance of anorectics. The microarray data presented in this study demonstrates the usefulness of gene expression analysis for gathering information on the efficacy and safety of anorectic drugs.

Analysis of Genes Regulated by HSP90 Inhibitor Geldanamycin in Neurons

  • ;;권오유
    • 대한의생명과학회지
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    • 제15권1호
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    • pp.97-99
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    • 2009
  • Geldanamycin is a benzoquinone ansamycin antibiotic that binds to cytosol HSP90 (Heat Shock Protein 90) and changes its biological function. HSP90 is involved in the intracellular important roles for the regulation of the cell cycle, cell growth, cell survival, apoptosis, angiogenesis and oncogenesis. To identify genes expressed during geldanamycin treatment against neurons of rats (PC12 cells), DNA microarray method was used. We have isolated 2 gene groups (up-or down-regulated genes) which are geldanamycin differentially expressed in neurons. Granzyme B is the gene most significantly increased among 204 up-regulated genes (more than 2 fold over-expression) and Chemokine (C-C motif) ligand 20 is the gene most dramatically decreased among 491 down-regulated genes (more than 2 fold down-expression). The gene increased expression of Cxc110, Cyp11a1, Gadd45a, Gja1, Gpx2, Ifua4, Inpp5e, Sox4, and Stip1 are involved stress-response gene, and Cryab, Dnaja1, Hspa1a, Hspa8, Hspca, Hspcb, Hspd1, Hspd1, and Hsph1 are strongly associated with protein folding. Cell cycle associated genes (Bc13, Brca2, Ccnf, Cdk2, Ddit3, Dusp6, E2f1, Illa, and Junb) and inflammatory response associated genes (Cc12, Cc120, Cxc12, Il23a, Nos2, Nppb, Tgfb1, Tlr2, and Tnt) are down-regulated more than 2 times by geldanamycin treatment. We found that geldanamycin is related to expression of many genes associated with stress response, protein folding, cell cycle, and inflammation by DNA microarray analysis. Further experimental molecular studies will be needed to figure out the exact biological function of various genes described above and the physiological change of neuronal cells by geldanamycin. The resulting data will give the one of the good clues for understanding of geldanamycin under molecular level in the neurons.

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다층퍼셉트론 기반 리 샘플링 방법 비교를 위한 마이크로어레이 분류 예측 에러 추정 시스템 (Classification Prediction Error Estimation System of Microarray for a Comparison of Resampling Methods Based on Multi-Layer Perceptron)

  • 박수영;정채영
    • 한국정보통신학회논문지
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    • 제14권2호
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    • pp.534-539
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    • 2010
  • 게놈 연구에서 수천 개의 특징들은 비교적 작은 샘플들로부터 모아진다. 게놈 연구의 목적은 미래 관찰들의 결과를 예측하는 분류기를 만드는 것이다. 분류기를 만들기 위해서는 특징 선택, 모델 선택 그리고 예측 평가 등의 3단계 과정을 거친다. 본 논문은 예측 평가에 초점을 맞추고 모든 슬라이드의 사분위수를 똑같게 맞추는 quantilenormalization 적용하여 마이크로어레이 데이터를 표준화 한 후 특징 선택에 앞서 예측 모델의 '진짜' 예측 에러를 평가하기 위해 몇 개의 방법들을 비교하는 시스템을 고안하고 방법들의 예측 에러를 비교 분석 하였다. LOOCV는 전체적으로 작은 MSE와 bias를 나타내었고, 크기가 작은 샘플에서 split 방법과 2-fold CV는 매우 좋지 않는 결과를 보였다. 계산적으로 번거로운 분석에 대해서는 10-fold CV가 LOOCV보다 오히려 더 낳은 경향을 보였다.

GEDA: New Knowledge Base of Gene Expression in Drug Addiction

  • Suh, Young-Ju;Yang, Moon-Hee;Yoon, Suk-Joon;Park, Jong-Hoon
    • BMB Reports
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    • 제39권4호
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    • pp.441-447
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    • 2006
  • Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and non-redundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.

Aquaporin 4 expression is downregulated in large bovine ovarian follicles

  • Kim, Chang-Woon;Choi, Eun-Ju;Kim, Eun-Jin;Siregar, Adrian S.;Han, Jaehee;Kang, Dawon
    • 한국동물생명공학회지
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    • 제35권4호
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    • pp.315-322
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    • 2020
  • Aquaporin channels (AQPs) are known to play an important role in the development of ovarian follicles through their function in water transport pathways. Compared to other AQPs, research on the role of AQP4 in female reproductive physiology, particularly in cattle, remains limited. In our previous study, gene chip microarray data showed a downregulation of AQP4 in bovine cystic follicles. This study was performed to validate the AQP4 expression level at the protein level in bovine follicles using immunohistochemistry, Western blotting, and immunoprecipitation assays. Immunostaining data showed that AQP4 was expressed in granulosa and theca cells of bovine ovarian follicles. The ovarian follicles were classified according to size as small (< 10 mm) or large (> 25 mm) in diameter. Consistent with earlier microarray data, semi-quantitative PCR data showed a decrease in AQP4 mRNA expression in large follicles. Western blot analysis showed a downregulation of the AQP4 protein in large follicles. In addition, AQP4 was immunoprecipitated and blotted with anti-AQP4 antibody in small and large follicles. Accordingly, AQP4 exhibited a low expression in large follicles. These results show that AQP4 is downregulated in bovine ovarian large follicles, suggesting that the downregulation of AQP4 expression may interfere with follicular water transport, leading to bovine follicular cysts.

고차원자료에서의 다중검정의 활용 (Multiple testing and its applications in high-dimension)

  • 장원철
    • Journal of the Korean Data and Information Science Society
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    • 제24권5호
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    • pp.1063-1076
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    • 2013
  • 현대 과학기술의 발전으로 빅데이터의 시대가 도래하였다, 이러한 빅데이터는 여러가지 과학적 문제에 대한 해답을 제공하지만 반면에 이로 인해 새로운 도전에 직면하고 있다. 마이크로어레이 자료와 같은 고차원자료는 이러한 빅데이터에서 흔히 볼 수 있는 유형중의 하나이다. 본 논문에서는 고차원 자료분석에 많이 쓰이고 있는 대역검정과 동시검정, 그리고 이의 응용에 대한 소개를 한다.

A Study on Two Group Comparison in Gene Expression Data

  • Seok, Kyung-Ha;Lee, Sangfeel;Bae, Whasoo
    • Communications for Statistical Applications and Methods
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    • 제11권2호
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    • pp.247-254
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    • 2004
  • Tusher, Tibshirani and Chu (2001) suggested SAM (Significance Analysis of Microarrays) to compare two groups under different conditions for each gene, using microarray data. They used two sample t-statistic adding fudge factor in the denominator to prevent the value of statistic from being inflated by large sample variance, which might result in significant difference despite of a small value in the numerator. This paper aims at finding robust fudge factor and replacing it in two-sample t-statistic used in SAM, which we call Modified SAM (MSAM). Using the simulated data and data used in Dudoit et al.(2002), it is shown that MSAM find significant genes better and has less error rate than SAM.

QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data

  • Kim, Nayoung;Park, Herin;He, Ningning;Lee, Hyeon Young;Yoon, Sukjoon
    • Genomics & Informatics
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    • 제10권4호
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    • pp.263-265
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    • 2012
  • We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

병렬 프로세서 기반의 패턴 분류 기법을 이용한 유전자 발현 데이터 분석 (Gene Expression Data Analysis Using Parallel Processor based Pattern Classification Method)

  • 최선욱;이종호
    • 전자공학회논문지CI
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    • 제46권6호
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    • pp.44-55
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    • 2009
  • 최근 활발히 연구가 진행 중인 마이크로어레이로부터 얻어지는 유전자 발현 데이터를 이용한 질병 진단은, 데이터를 직접적으로 분석하기 힘들기 때문에 일반적으로 기계 학습 알고리즘을 사용하여 이루어져왔다. 그러나 유전자 발현 데이터를 분석함에 있어서 유전자들 간의 상호작용을 고려하는 분석이 필요하다는 최근의 연구 결과들은 기존 기계 학습 알고리즘들을 이용한 분석에 한계가 있음을 의미한다고 볼 수 있다. 본 논문에서는 특징들 사이의 고차원 상관관계를 고려 가능한 하이퍼네트워크 모델을 이용하여 유전자 발현 데이터의 분류를 수행하고 기존의 기계 학습 알고리즘들과 분류 성능을 비교한다. 또한 기존 하이퍼네트워크 모델의 단점을 개선 한 모델을 제안하고, 이를 병렬 프로세서 상에서 구현하여 처리 성능을 비교한다. 실험 결과 제안 된 모델은 기존의 기계 학습 방법들과의 비교에서도 경쟁력 있는 분류 성능을 보여주었고, 기존 하이퍼네트워크 모델 보다 안정적이고 향상된 분류 성능을 보여주었다. 또한 이를 병렬 프로세서 상에서 구현 할 경우 처리 성능을 극대화 할 수 있음을 보였다.