• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.1803-1808
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• 2012

This study aimed to investigate the effects of Ser/Cys polymorphism in hOGG1 gene, Arg/Pro polymorphism in p53 gene, smoking and their interactions on the development of lung cancer. Ser/Cys polymorphism in hOGG1 and Arg/Pro polymorphism in p53 among 124 patients with lung cancer and 128 normal people were detected using PCR-RFLP. At the same time, smoking status was investigated between the two groups. Logistic regression was used to estimate the effects of Ser/Cys polymorphism and Arg/Pro polymorphisms, smoking and their interactions on the development of lung cancer. ORs (95% CI) of smoking, hOGG1 Cys/Cys and p53 Pro/Pro genotypes were 2.34 (1.41-3.88), 2.12 (1.03-4.39), and 2.12 (1.15-3.94), respectively. The interaction model of smoking and Cys/Cys was super-multiplicative or multiplicative, and the OR (95% CI) for their interaction item was 1.67 (0.36 -7.78). The interaction model of smoking and Pro/Pro was super-multiplicative with an OR (95%CI) of their interaction item of 5.03 (1.26-20.1). The interaction model of Pro/Pro and Cys/Cys was multiplicative and the OR (95%CI) of their interaction item was 0.99 (0.19-5.28). Smoking, hOGG1 Cys/Cys, p53 Pro/Pro and their interactions may be the important factors leading to the development of lung cancer.

• 정보처리학회논문지B
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• 제14B권5호
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• pp.383-390
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• 2007

단백질이나 유전자들 간의 상호작용 인식은 생물학적 현상의 기술에 있어서 필수적이고, 이러한 상호작용의 네트웍 파악은 생물학 접근의 시작이라고 할 수 있다. 최근에, 대량의 생물학 관련 문서로부터 자연언어처리 기술을 사용하여 이러한 정보를 추출하려는 연구들이 많이 등장했다. 또한 이전 연구들은 언어학적 정보가 문서로부터 유전자 상호작용을 자동으로 추출하는 데 있어서 유용하다고 주장하고 있다. 하지만 기존의 방법들은 정확률에 비해 재현율이 많이 낮아서 성능이 그다지 좋지 못했다. 정확률의 감소 없이 재현율의 성능향상을 위해, 이 논문은 생물학관련 문서에서 구문관계에 기반하여 유전자 상호작용을 인식하는 방법을 제안한다. 생물학 도메인에 관련된 전문지식 없이, 우리의 방법은 단지 적은 양의 학습데이터를 사용하여 효과적인 성능을 보인다. LLL05(ICML05 Workshop on Learning Language in Logic)에서 제공한 데이터 포맷을 그대로 사용하여, 상호작용하는 두 유전자 중 작용의 주체가 되는 유전자를 에이전트라 하고 상호 작용의 대상이 되는 유전자를 타겟이라 한다. 본 논문에서 제안하는 첫 단계에서, 에이전트와 타겟 유전자에 대한 유전자-전이 구문관계를 인식한다. 두 번째 단계에서, 유전자 간의 상호작용이 있음을 암시하는 용언리스트를 구축한다. 마지막 단계에서, 상호작용하는 것으로 인식된 두 유전자 중 어느 것이 에이전트이고 타겟인지를 판단하기 위해 구문관계의 방향 정보를 학습한다. LLL05 데이터를 사용한 실험결과에서, 본 논문에서 제안한 방법이 학습 데이터에 대해서는 88%의 F-measure 성능을 보였고, 테스트 데이터에 대해서는 70.4%의 F-measure 성능을 보였다. 이 결과는 기존의 방법들보다 훨씬 더 좋은 성능이다. 우리는 성능에 대한 각 단계의 공헌도를 실험하여, 첫 단계는 재현율 향상에 기여를 하고 두 번째와 세 번째 단계는 정확률 향상에 기여했음을 보인다.

• Journal of Microbiology and Biotechnology
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• 제18권4호
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• pp.631-638
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• 2008

Tandem affinity purification (TAP) method combined with LC-MS/MS is the most accurate and reliable way to study the interaction of proteins or proteomics in a genome-wide scale. For the first time, we used a TAP-tag as a mutagenic tool to disrupt protein interactions at the specific site. Although lots of commonly used mutational tools exist to study functions of a gene, such as deletional mutations and site-directed mutagenesis, each method has its own demerit. To test the usefulness of a TAP-tag as a mutagenic tool, we applied a TAP-tag to RNA polymerase II, which is the key enzyme of gene expression and is controlled by hundreds of transcription factors even to transcribe a gene. Our experiment is based on the hypothesis that there will be interrupted interactions between Pol II and transcription factors owing to the TAP-tag attached at the C-terminus of each subunit of Pol II, and the abnormality caused by interrupted protein interactions can be observed by measuring a cell-cycle of each yeast strain. From ten different TAP-tagged strains, Rpb7- and Rpb12-TAP-tagged strains show severe defects in growth rate and morphology. Without a heterodimer of Rpb4/Rpb7, only the ten subunits Pol II can conduct transcription normally, and there is no previously known function of Rpb7. The observed defect of the Rpb7-TAP-tagged strain shows that Rpb7 forms a complex with other proteins or compounds and the interruption of the interaction can interfere with the normal cell cycle and morphology of the cell and nucleus. This is a novel attempt to use a TAP-tag as a proteomic tool to study protein interactions.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.227-232
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• 2010

In this paper we consider the multi-robot system that collects target objects spread in an unexplored environment. The robots cooperate each other to improve the capability and the efficiency. The robots attract or intimidate each other as behaviors of bacterial swarms or particles with electrical moments. The interactions would increase the working efficiency in some environments but it would decrease the efficiency in some other environments. Therefore, the system needs to adapt to the working environment by adjusting the strengths of the interactions. The strengths of the interactions are expressed as sets of gene codes that mean the weights of each kind of attracting or intimidating vectors. The proposed system adjusts the gene codes using evolutional strategy. The proposed approach has been validated by computer simulation. The results of this paper show that our inter-swarm interacting strategy and optimizing algorithm improves the working efficiency, adaptively to the characteristics of environments.

• 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.

• Bioinformatics and Biosystems
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• 제2권1호
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• pp.12-16
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• 2007

DNA repair means a collection of processes that a cell identifies and corrects damage to genome sequence. The DNA repair processes are important because a genome would not be able to maintain its essential cellular functions without the processes. In this research, we make some gene regulatory networks of DNA repair in S. cerevisiae to know how each gene interacts with others. Two approaches are adapted to make the networks; Bayesian Network and ARACNE. After construction of gene regulatory networks based on the two approaches, the two networks are compared to each other to predict which genes have important roles in the DNA repair processes by finding conserved interactions and looking for hubs. In addition, each interaction between genes in the networks is validated with interaction information in S. cerevisiae genome database to support the meaning of predicted interactions in the networks.

• Bulletin of the Korean Chemical Society
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• 제18권1호
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• pp.18-23
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• 1997

Data from structure/activity studies in vir gene induction system have led to evaluate the working hypothesis of interaction between phenolic inducers and phenol binding proteins. The primary specificity in the association of a phenolic inducer with its receptor in our system is hypothesized to be the hydrogen bonding interactions through the ortho methoxy substituents as well as the proton transfer between the inducer and the binding protein. In this paper the proposed working model for phenol-mediating signal transduction was evaluated in several ways. The importance of the general acid-base catalysis was first addressed by the presence of an acidic residue and a basic residue in the phenol binding protein. Series of compounds were tested for vir gene expression activity to confirm the generation of a strong nucleophile by an acidic residue and an involvement of a basic residue as a proton acceptor. An attempt was made to correlate the pKa values of the phenolic compounds with vir gene induction activities as inducers to further support the proposed proton transfer mechanism. Finally, it was also observed that the regioselectively attached methoxy group on phenol compounds is required as the proper hydrogen bond acceptor.

• Genomics & Informatics
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• 제18권4호
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.31-36
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• 2005

An identification and characterization of susceptibility genes for common complex multifactorial diseases is a challengeable task, in which the effect of single genetic variation will be likely dependent on other genetic variations(gene-gene interaction) and environmental factors (gene-environment interaction). To address is issue, the multifactor dimensionality reduction (MDR) has been proposed and implemented by Ritchie et al. (2001), Moore et al. (2002), Hahn et al.(2003) and Ritchie et al. (2003). With MDR, multilocus genotypes effectively reduce the dimension of genotype predictors from n to one, which improves the identification of polymorphism combinations associated with disease risk. However, MDR cannot handle missing observations appropriately, in which missing observation is treated as an additional genotype category. This approach may suffer from a sparseness problem since when high-order interactions are considered, an additional missing category would make the contingency table cells more sparse. We propose a new MDR approach with minimum loss of sample sizes by considering missing data over all possible multifactor classes. We evaluate the proposed MDR by using the prediction errors and cross validation consistency.

• 농업과학연구
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• 제48권1호
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• pp.151-161
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• 2021

Fusarium wilt disease of tomato plants caused by Fusarium oxysporum f.sp. lycopersici (FOL race2) is one of the most important diseases of tomatoes worldwide. In the competition between tomato and FOL, the FOL can win by overcoming the immune system of tomato plants. Resistant interaction between the FOL race2 and tomato plants is controlled by avirulence genes (AVR2) in FOL and the corresponding resistance genes (I2) in tomato plants. In this study, 7 FOL isolates (KACC) were used to test their pathogenicity, and FOL race2 was selected because it is a broad problem in Korea. The Fol40044 isolates showed the most severe pathogenicity, and the avr2 gene was also isolated and identified. Moreover, to select resistance, 20 tomato varieties were inoculated with the Fol40044, and the degree of pathogenicity was evaluated by analyzing the expression of the avr2 gene. As a result, three resistant tomato varieties (PCNUF73, PCNUF101, PCNUF113) were selected, and the expression of the avr2 gene was much lower than that of the control Heinz cultivar. This result shows that the screening assay is very efficient when the avr2 gene is used as a marker to evaluate the expression level when selecting varieties resistant to tomato wilt disease. Based on these results, it is possible to isolate the I2 gene, which exhibits resistance and molecular biological interactions with the AVR2 gene from the three tomato-resistant varieties. The I2 gene provides breeders more opportunities for Fusarium disease resistance and may contribute to our understanding of their interactions with the FOL and host plant.