• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.3952-3961
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• 2012

Developing computational methods for identifying cell cycle-regulated genes has been one of important topics in systems biology. Most of previous methods consider the periodic characteristics of expression signals to identify the cell cycle-regulated genes. However, we assume that cell cycle-regulated genes are relatively active having relatively many interactions with each other based on the underlying cellular network. Thus, we are motivated to apply the theory of multivariate phase synchronization to the cell cycle expression analysis. In this study, we apply the method known as "Self-Organizing Maps with statistical Phase Synchronization (SOMPS)", which is the combination of self-organizing map and multivariate phase synchronization, producing several subsets of genes that are expected to have interactions with each other in their subset (Kim, 2008). Our evaluation experiments show that the SOMPS algorithm is able to detect cell cycle-regulated genes as much as one of recently reported method that performs better than most existing methods.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.197-207
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• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.

• Horticultural Science & Technology
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• v.32 no.5
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• pp.684-693
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• 2014

Abiotic stress conditions such as cold, drought, and salinity trigger physiological and morphological changes and yield loss in plants. Hence, plants adapt to adverse environments by developing tolerance through complex regulation of genes related to various metabolic processes. This study was conducted to construct a coexpression network for multidirectional analysis of salt-stress response genes in Brassica rapa (Chinese cabbage). To construct the coexpression network, we collected KBGP-24K microarray data from the B. rapa EST and microarray database (BrEMD) and performed time-based expression analyses of B. rapa plants. The constructed coexpression network model showed 1,853 nodes, 5,740 edges, and 142 connected components (correlation coefficient > 0.85). On the basis of the significantly expressed genes in the network, we concluded that the development of salt tolerance is closely related to the activation of $Na^+$ transport by reactive oxygen species signaling and the accumulation of proline in Chinese cabbage.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.690-692
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• 2022

기존의 질병 관련 연구들은 대부분 유의미하게 변화되는 유전자들을 찾아내고(Differentially Expressed Genes, DEGs), 이들이 연관된 생물학적 패스웨이(biological pathway)를 찾아내는 방향으로 이루어졌다. 더불어 miRNA(microRNA)가 많은 mRNA 의 발현을 조절하며, 실제 면역, 대사 및 세포 사멸을 포함한 여러 필수 생리학적 및 질병에 매우 중요한 역할을 한다고 밝혀지며, 바이오 마커로써의 miRNA 를 찾아내고자 하는 연구가 활발히 진행되기 시작하였다. 하지만 mRNA 나 miRNA 의 독립적인 연구만으로는 명확한 질병과의 연관성이나 기능을 이해하기에는 어려움이 있다. 따라서 본 연구에서는 질병 상태에서 유의미하게 변화되는 miRNA 와 이러한 miRNA 에 의해 조절되는 mRNA 를 함께 고려하여 분석함으로써, 실제 질병의 발병 원인이 되는 생물학적 패스웨이나 메커니즘을 밝히고자 하였다. 또한, miRNA 와 mRNA 의 연관성을 찾기 위해, PPI(protein-protein interaction) 네트워크에 기반을 둔 RWR(Random Walk with Restart Algorithm)를 적용하여, 직접적 연관성뿐 아니라, 유전자 간의 숨겨진 간접적인 패스웨이를 고려하여 분석하기 위한 웹 기반 시스템을 개발하였다. 이 시스템은 mRNA-miRNA 를 함께 고려한 통합 분석을 통해 숨겨진 질병의 메커니즘을 이해하고 치료 방법을 찾아내는 데 크게 공헌할 것이다.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.932-937
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• 2007

생의학 분야 문헌의 양이 빠르게 증가함에 따라, 생의학 연구자들이 필요로 하는 정보를 얻기가 어렵게 되었다. 이를 해결하기 위해, 인간-컴퓨터 상호작용 분야에서는 생의학 문헌 검색 시스템, 또는 생의학 문헌의 정보 추출 시스템 등에 대한 연구가 진행되고 있다. 본 논문에서는 생의학 문헌으로부터 정보를 자동으로 추출하기 위한 관계정보 추출 시스템에 대해 소개한다. 소개하는 시스템은 크게 요약 수집 모듈, 관계 추출 모듈, 관계 가시화 모듈로 구성되어 있다. 우선, 요약 수집 모듈에서는 특정 주제의 문헌들을 검색 및 수집한다. 그리고, 관계 추출 모듈에서는 수집된 문헌들에 대해서, 단백질/유전자 등의 생물학 개체를 인식하고, 구문분석을 통하여 인식된 개체들 사이의 관계를 추출한다. 마지막으로, 관계 가시화 모듈에서는 추출된 관계를 통합하여 네트워크 형태로 가시화한다. 이 시스템은 생물학 실험 이전의 문헌 기반 타당성 검사, 단백질-단백질 상호작용 또는 특정 질병과 유전자의 조절관계 분석, 또는 대용량 문헌 처리를 통한 패스웨이 데이터베이스 구축 등에 활용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.6-10
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• 2006

마이크로어레이 실험의 등장으로 한 번에 수백 개에서 수천 개의 유전자를 실험할 수 있게 되었다. 이는 기존의 실험과 비교했을 때 질적인 측면과 양적인 측면에서 가히 혁신적이라 할 수 있다. 마이크로어레이 칩을 이용한 실험에서 쏟아져 나오는 엄청난 데이터를 비교, 분석, 관리하기 위해서는 실험실의 마이크로어레이 분석 소프트웨어나 시스템간의 데이터 형식이 호환되어야 하며, 소프트웨어의 지원 또한 획기적이고 효율적이어야 한다. 본 논문에서는 다양한 종류의 마이크로어레이 입력 데이터 및 분석 데이터를 다룰 수 있고, 표준 파일 형식으로의 변환 기능을 제공하며, 마이크로어레이 이미지 분석용 소프트웨어인 ArrayMall[1,2]과 유전자 조절 네트워크 분석 시스템인 GENAW[3]를 통합하고 마이크로어레이 실험데이터의 분석, 관리 및 데이터 공유를 위한 분산 시스템인 SMILE[4]에 대해 소개한다.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.3
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• pp.120-132
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• 2008

Networks-on-Chip (NoC) is emerging as a practical development platform for future systems-on-chip products. We propose an energy-efficient static algorithm which optimizes the energy consumption of task communications in NoCs with voltage scalable links. In order to find optimal link speeds, the proposed algorithm (based on a genetic formulation) globally explores the design space of NoC-based systems, including network topology, task assignment, tile mapping, routing path allocation, task scheduling and link speed assignment. Experimental results show that the proposed design technique can reduce energy consumption by 28% on average compared with existing techniques.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of Life Science
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• v.33 no.1
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• pp.34-42
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• 2023

In a signal transduction network, from the perception of stress signals to stress-responsive gene ex- pression, binding of various transcription factors to cis-acting elements in stress-responsive promoters coordinate the adaptation of plants to abiotic stresses. Among the AP2/ERF transcription factor family genes, group VII ERF genes, such as RAP2.12, RAP2.2, RAP2.3, AtERF73/HRE1, and AtERF71/ HRE2, are known to be involved in the response to hypoxia stress in Arabidopsis. In this study, we dissected the HRE1 promoter to identify hypoxia-responsive region(s). The 1,000 bp upstream promoter region of HRE1 showed increased promoter activity in Arabidopsis protoplasts and transgenic plants under hypoxia conditions. Analysis of the promoter deletion series of HRE1, including 1,000 bp, 800 bp, 600 bp, 400 bp, 200 bp, 100 bp, and 50 bp upstream promoter regions, using firefly luciferase and GUS as reporter genes indicated that the -200 to -100 region of the HRE1 promoter is responsible for the transcriptional activation of HRE1 in response to hypoxia. In addition, we identified two putative hypoxia-responsive cis-acting elements, the ERF-binding site and DOF-binding site, in the -200 to -100 region of the HRE1 promoter, suggesting that the expression of HRE1 might be regulated via the ERF transcription factor(s) and/or DOF transcription factor(s). Collectively, our results suggest that HRE1 contains hypoxia-responsive cis-acting elements in the -200 to -100 region of its promoter.

• Korean Journal of Microbiology
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• v.45 no.1
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• pp.10-16
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• 2009

A diverse range of stresses such as heat, cold, salt, ethanol, oxygen starvation or nutrient starvation induces same stress-responsive proteins. This general stress response enhances bacterial survival significantly. In Bacillus subtilis and closely related Gram-positive bacteria Listeria monocytogenes, the general stress response is controlled by the alternative transcription factor ${\sigma}^B$. The activity of ${\sigma}^B$ is regulated post-translationally by a signal transduction network that has been extensively studied in B. subtilis, and serve as a model for L. monocytogenes. The proposed model of L. monocytogenes signal transduction network is similar to that of B. subtilis, but the energy stress pathway is missing. More than 150 general stress proteins belong to ${\sigma}^B$ regulon of B. subtilis and L. monocytogenes. In both bacteria, ${\sigma}^B$ function is primarily important for resistance to diverse stresses. In addition, ${\sigma}^B$ function contributes to the control of important virulence genes in food-borne pathogen L. monocytogenes. Therefore, understanding of the general stress response is important not only for bacterial physiology, but also for pathogenicity.