• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1189-1195
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• 2004

This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.161-164
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• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• 응용통계연구
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• 제18권2호
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• pp.371-379
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• 2005

본 논문에서는 피어슨 상관계수를 이용한 시간경로 유전자 발현자료의 군집분석에서 군집의 대표적인 패턴에서 벗어나는 이질적인 패턴을 보이는 시계열을 탐지하기 위한 패턴일치지수를 제안하고, 이를 마이크로어레이 실험으로부터 얻어진 혈청 시간경로 유전자 발현자료에 적용하여 유용성을 검토해 본다.

• 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.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• 정보처리학회논문지D
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• 제14D권1호
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• pp.9-20
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• 2007

유전자들의 그룹은 복잡한 상호작용들을 통해 세포의 기능이 조절되며 이러한 상호작용을 하는 유전자 그룹들을 유전자 조절 네트워크 (GRNs: Gene Regulatory Networks)라고 한다. 이전의 유전자 발현 분석 기법인 군집화와 분류는 단지 상동성에 의한 유전자들 사이의 소속을 결정하는 데에는 유용하나 분자 활동에서의 같은 클래스에서 발견되어지는 유전자들 사이의 조절 관계를 식별할 수 없다. 더욱이 유전자들이 어떻게 연관되는 지와 유전자들이 서로 어떻게 조절하는지에 대한 매커니즘의 이해가 필요하다. 따라서 이 논문에서는 시계열 마이크로어레이 데이터로부터의 유전자들의 조절 관계를 발견하기 위해서 빈발 패턴 마이닝과 연쇄 규칙을 이용한 새로운 접근법을 제안하였다. 이 기법에서는 먼저, 빈발 패턴 마이닝 적용을 위한 적절한 데이터 변환 방법을 제안하였고 FP-growth을 이용하여 유전자 발현 패턴들을 발견한다. 그런 다음, 연쇄 규칙을 이용하여 빈발한 유전자 패턴들로부터 유전자 조절 네트워크를 구축하였다. 마지막으로 제안된 기법의 검증은 공개된 유전자들의 조절 관계와 실험 결과의 일치함을 보임으로써 평가하였다.

• International Journal of Contents
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• 제8권1호
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• pp.23-29
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• 2012

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

• Mycobiology
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• 제50권1호
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• pp.96-98
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• 2022

The Aspergilli of the section Nidulantes series Versicolores are among the most recurrent molds in indoor environments. These species cause damage to the quality of air. Indeed, they are responsible for allergies, aggravation of asthma and can even cause infections in immunocompromised patients. Molds belonging to the Versicolores series also produce sterigmatocystin, a mycotoxin classified as potential human carcinogen by the International Agency for Research on Cancer (group 2B). Here, we provide for the first time the genome of three species of the series Versicolores: Aspergillus creber, Aspergillus jensenii and Aspergillus protuberus which are the most abundant species of this series in bioaerosols. The genomes of these three species could be assembled with a percentage of completeness of 97.02%, 96.21% and 95.35% for Aspergillus creber, A. jensenii and A. protuberus respectively. These data will allow to study the genes and gene clusters responsible for the expression of virulence factors, the biosynthesis of mycotoxins and the proliferation of these ubiquitous and recurrent molds.

• Asian Pacific Journal of Cancer Prevention
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• 제15권4호
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• pp.1783-1789
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• 2014

Background: The EMSY gene encodes a BRCA2-binding partner protein that represses the DNA repair function of BRCA2 in non-hereditary breast cancer. Although amplification of EMSY gene has been proposed to have prognostic value in breast cancer, no data have been available concerning EMSY tissue expression patterns and its associations with clinicopathological features. Materials and Methods: In the current study, we examined the expression and localization pattern of EMSY protein by immunohistochemistry and assessed its prognostic value in a well-characterized series of 116 unselected breast carcinomas with a mean follow up of 47 months using tissue microarray technique. Results: Immunohistochemical expression of EMSY protein was detected in 76% of primary breast tumors, localized in nuclear (18%), cytoplasmic (35%) or both cytoplasmic and nuclear sites (23%). Univariate analysis revealed a significant positive association between EMSY expression and lymph node metastasis (p value=0.045) and larger tumor size (p value=0.027), as well as a non-significant relation with increased risk of recurrence (p value=0.088), whereas no association with patients' survival (log rank test, p value=0.482), tumor grade or type was observed. Conclusions: Herein, we demonstrated for the first time the immunostaining pattern of EMSY protein in breast tumors. Our data imply that EMSY protein may have impact on clinicipathological parameters and could be considered as a potential target for breast cancer treatment.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 추계학술발표대회
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• pp.733-736
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• 2007

최근 생명 공학 기술의 발달로 마이크로 단위의 실험이 가능해지고 하나의 칩상에 수 만개의 유전자들의 발현 양상을 보다 쉽게 관찰할 수 있게 되었다. DNA 칩 기술에 의해 얻어지는 마이크로어레이(microarray) 데이터는 세포나 조직 내의 유전자 발현도(expression level)를 측정한 것으로 질병 진단이나 유전자 기능 예측 등에 이용되고 있다. 본 논문에서는 대량의 시계열 마이크로어레이 데이터 분석을 위해 효율적으로 데이터의 차원을 판단하는 점진적 주성분 분석을 이용하여 데이터의 차원을 축소 한다. 제안된 방법은 실제 시계열 마이크로어레이 데이터인 yeast cell cycle 데이터에 적용되었고, 데이터 차원 축소에 대한 효율성을 검증하기 위해 클러스터링을 수행하였다. 그 결과 데이터를 축소하여 클러스터링을 수행한 경우 학습 성능이 향상 된 결과를 보였다.