• Reproductive and Developmental Biology
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• v.31 no.3
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• pp.207-213
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• 2007

This study was carried out to examine gene expression altered in endometrium of Korean cattle (Hanwoo) with endometritis using microarray. In this study, 4,560 diferentially expressed genes (DEGs) were identified in the endometrium of Hanwoo. Of 4,560 DEGs, 2,026 genes were up-regulated, while 2,536 genes were down-regulated in endometritis. Of them, top 10 regulated genes were listed. Filamin A, pancreatic anionic trypsinogen, Rho GDP dissociation inhibitor alpha, collagen type VI alpha 1, butyrate response factor 2, aggrecanses-2, annexin 14, aminopeptidease A, orphan transporter v7-3, and epithelial stromal interaction 1 were up-regulated, while MHC class II antigen, integrin-binding sialoprotein, uterine milk protein precursor, down-regulated in colon cancer 1, glycoprotein 330, dickkopf-1, cfh protein, $Ca^{2+}-dependent$ secretion activator, UL16 binding protein 3, and proenkephalin were down-regulated in the endometritis. Our results suggest that these genes could be useful biomarkers for diagnosis Hanwoo's endometritis.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.8-16
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• 2006

DNA microarray 기술은 동시적으로 수천 개의 유전자의 발현상황을 탐색할 수 있다. 이 기술을 통해 얻어진 자료는 분석하기에 앞서 전처리 과정으로 배경보정 (background correction), 표준화 (normalization) 그리고 요약 (summarization)이 필요하다. 표준화란 microarray 실험에서 기술상의 문제로 첨가되는 일정한 잡음을 인식, 제거하기 위해 필요한 기법으로 그 동안 여러 방법들이 제시되어 왔다. 또한 마이크로어레이 자료의 분석을 위한 요약 방법으로도 많은 방법들이 연구되었다. 본 글에서는 표준화 방법들과 요약 방법들의 특성을 분석, 비교하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.277-279
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• 2005

DNA 마이크로어레이 기술의 발전으로 유전자 발현에 대한 많은 양의 정보가 쏟아지게 되었고, 이러한 정보들을 이용하여 유전자 조절 네트워크를 수학적으로 모델링하는 것이 시스템 생물학의 중요 관심사로 떠오르고 있다. 본 논문에서는 실험에서 얻어낸 데이터를 유전 프로그래밍을 이용한 기호 회귀를 통해 데이터 지점을 조정하고 유전 프로그래밍의 결과 함수를 이용해 각 지점에서의 미분값을 얻어내었다. 그 뒤, 불리안 네트워크를 표현하는 이진 배열과 S-시스템을 표현하는 실수 배열을 결합한 해를 사용하는 유전 알고리즘으로 앞에서 얻은 데이터를 이용해 원하는 S-시스템의 구조와 매개변수를 구해내었다.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.91-99
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• 2014

The objective of this study is to identify cold-tolerance genes in Brassica rapa. In order to acheive this goal, we analyzed a KBGP-24K oligo chip data [BrEMD (B. rapa EST and Microarray Database)] using B. rapa ssp. pekinensis inbred line 'Chiifu' under cold stress condition ($4^{\circ}C$). Among 23,929 unigenes of B. rapa, 417 genes (1.7%) were primarily identified as cold responsive genes that were expressed over 5-fold higher than those of wild type control, and then a gene which has unknown function and has full length sequence was selected. It was named BrCSR (B. rapa Cold Stress Resistance). BrCSR was transformed using expression vector pSL101 to confirm whether BrCSR can enhance cold tolerance in tobacco plants. $T_1$ transgenic tobacco plants expressing BrCSR were selected by PCR and Southern hybridization analyses, and the function of BrCSR was characterized by expression level analysis and phenotype observation under cold stress condition. The expression level of BrCSR in transgenic tobacco plants increased up to about two folds in quantitative real-time RT-PCR assay and this was very similar to Northern blot hybridization analysis. Analysis of phenotypic characteristics clearly elucidated that transgenic tobaccos expressing BrCSR were more cold tolerant than wild type control under $4^{\circ}C$ treatment. Based on these results, we conclude that the over-expression of BrCSR might be closely related to the enhancement of cold tolerance.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.617-626
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• 2009

A series of recent papers reported that the inter-gene correlations in Affymetrix microarray data sets were strong and long-ranged, and the assumption of independence or weak dependence among gene expression signals which was often employed without justification was in conflict with actual data. Qui et al. (2005) indicated that applying the nonparametric empirical Bayes method in which test statistics were pooled across genes for performing the statistical inference resulted in the large variance of the number of differentially expressed genes. Qui et al. (2005) attributed this effect to strong and long-ranged inter-gene correlations. Klebanov and Yakovlev (2007) demonstrated that the inter-gene correlations provided a rich source of information rather than being a nuisance in the statistical analysis and they developed, by transforming the original gene expression sequence, a sequence of independent random variables which they referred to as a ${\delta}$-sequence. We note in this report using two cDNA microarray data sets experimented in this country that the strong and long-ranged inter-gene correlations were still valid in cDNA microarray data and also the ${\delta}$-sequence of independence could be derived from the cDNA microarray data. This note suggests that the inter-gene correlations be considered in the future analysis of the cDNA microarray data sets.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.210-219
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• 2003

오페론(operon)은 보통 미생물에서 다수의 인접한 유전자들로 구성된 그룹으로 하나의 유전자처럼 공통된 프로모터에 의해 전사되는 단위이다. 오페론을 구성하는 유전자들은 기능적으로 서로 유사하거나 같은 물질대사경로(metabolic pathway) 상에 존재하는 특징을 지니기 때문에 이들은 중요한 의미를 가지며, 미생물 유전체 분석에서 오페론을 구성하는 유전자들을 예측하는 것은 상당히 중요하다. 오페론을 예측하는 이전 연구들로는 이미 알려진 오페론의 특징인 유전자간 거리나 오페론을 구성하는 평균 유전자 개수 등을 이용하는 방법, 마이크로어레이 발현 실험을 이용한 방법, 전유전체(whole genome)들 간의 보존된 유전자 집합(conserved gene cluster)을 이용한 방법 그리고 물질대사경로를 이용한 방법 등이 있다. 본 논문에서는 COG 기능(function) 거리, 유전자 간의 거리, 코돈 사용빈도(codon usage) 그리고COG 기능 거리와 유전자간 거리를 같이 적용한 방법을 이용하여 오페론 예측을 위한 전처리 모델을 생성하였다 전처리 모델을 E. coli 전유전체에 적용해본 결과, 알려진 오페론들의 약 90%가 이를 포함하였다. 따라서 본 논문에서 제시한 전처리 모델은, 추후 오페론 예측을 위한 좋은 도구로 활용할 수 있을 것이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2259-2264
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• 2008

Significant genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect informative genes by similarity scale combination method being proposed in this paper after normalizing data with methods that are the most widely used among several normalization methods proposed the while. And it compare and analyze a performance of each of normalization methods with multi-perceptron neural network layer. The Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) after Lowess normalization represented the improved classification performance of 98.84%.

• Horticultural Science & Technology
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• v.32 no.6
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• pp.845-852
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• 2014

Among various abiotic stress factors, soil salinity decreases the photosynthetic rate, growth, and yield of plants. Recently, many genes have been reported to enhance salt tolerance. The objective of this study was to characterize the Brassica rapa Salt Stress Resistance (BrSSR) gene, of which the function was unclear, although the full-length sequence was known. To characterize the role of BrSSR, a B. rapa Chinese cabbage inbred line ('CT001') was transformed with pSL94 vector containing the full length BrSSR cDNA. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis showed that the expression of BrSSR in the transgenic line was 2.59-fold higher than that in the wild type. Analysis of phenotypic characteristics showed that plants overexpressing BrSSR were resistant to salinity stress and showed normal growth. Microarray analysis of BrSSR over-expressing plants confirmed that BrSSR was strongly associated with ERD15 (AT2G41430), a gene encoding a protein containing a PAM2 motif (AT4G14270), and GABA-T (AT3G22200), all of which have been associated with salt tolerance, in the co-expression network of genes related to salt stress. The results of this study indicate that BrSSR plays an important role in plant growth and tolerance to salinity.

• Journal of KIISE
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• v.45 no.1
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• pp.61-68
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• 2018

The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.