• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Genomics & Informatics
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• 제20권1호
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5005-5006
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• 2015

Gastric cancer as one of the most common cancers worldwide has various genetic and environmental risk factors including Helicobacter pylori (H.pylori) infection. Recently, loss of a tumor suppressor gene named promyelocytic leukemia (PML) has been identified in gastric cancer. However, no mutation has been found in this gene in gastric cancer samples. Cag A H.pylori protein has been shown to exert post transcriptional regulation of some tumor suppressor genes. In order to assess such a mechanism for PML degradation, we performed in silico analyses to establish any interaction between PML and Cag A proteins. In silico interaction and docking studies showed that these two proteins may have stable interactions. In addition, we showed that imatinib kinase inhibitor can restore PML function by inhibition of casein kinase 2.

• 식물병연구
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• 제25권1호
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• pp.1-7
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• 2019

벼도열병균은 벼를 재배하는 모든 지역에서 경제적으로 매우 중요한 병이다. 또한, 벼도열병균은 기주인 벼와 유전자 대유전자설이 적용되는 대표적인 식물병원균 모델이다. 재배지에 도입된 새로운 저항성 벼 품종의 빠른 저항성 상실은 병원균 집단의 레이스 변이가 주요 메커니즘으로 제안되고 있다. 이러한 새로운 레이스 변이는 저항성 유전자에 대항하는 비병원성 유전자의 변이에 의해 나타날 수 있는데, (i) 점돌연변이, (ii) 전이인자(transposon)의 삽입, (iii) frame shift등이 그 대표적인 예라고 할 수 있다. 비병원성 유전자 AVR-Pita1은 이러한 다양한 변이의 원인들이 모두 보고된 대표적인 비병원성 유전자이다. 이 총설에서는 비병원성 유전자 AVR-Pita1에 관한 다양한 정보를 제시하고, 상동성 유전자들인 AVR-Pita2 및 AVR-Pita3 유전자를 정리하였다. 이와 함께, 변이의 원인이 되는 다양한 예제를 리뷰 하였다.

• Environmental Analysis Health and Toxicology
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• 제20권4호통권51호
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• pp.303-309
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• 2005

Essential hypertension has been considered as multifactorial disease resulted from the interaction of both environmental and genetic factors. The renin-angiotensin system (RAS) plays an important role in the regulation of blood pressure homeostasis. Recently, a homologue of angiotensin I converting enzyme, ACE2 has been focused on as a candidate gene of essential hypertension in the experiments using animal model and human being. In this study, we carried out an association study in order to clarify the relationship between the A 1075G polymorphism in the ACE2 gene and essential hypertension in Korean subjects. Because this polymorphism is located on human chromosome X, the statistical analysis for each gender was performed separately. There were no significant differences in allele distribution of the A 1075G polymorphism in the ACE2 gene between normotensives and hypertensives in the both gender groups, respectively. However, this polymorphism was significantly associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) values in only female groups (P< 0.05). Thus, these results may suggest the probable role of ACE2 gene in the inter-individual susceptibility of female group to blood pressure variability.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권10호
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• pp.397-402
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• 2019

암 환자에게 적절한 치료계획을 제공하기 위해 암의 진행양상 또는 환자의 생존 기간 등에 해당하는 환자의 예후를 정확히 예측하는 것은 생물정보학 분야에서 다루는 중요한 도전 과제 중 하나이다. 많은 연구에서 암 환자의 유전자 발현량 데이터를 이용하여 환자의 예후를 예측하는 기계학습 모델들이 많이 제안되어 오고 있다. 유전자 발현량 데이터는 약 17,000개의 유전자에 대한 수치값을 갖는 고차원의 수치형 자료이기에, 기존의 연구들은 특징 선택 또는 차원 축소 전략을 이용하여 예측 모델의 성능 향상을 도모하였다. 그러나 이러한 접근법은 특징 선택과 예측 모델의 훈련이 분리되어 있어서, 기계학습 모델은 선별된 유전자들이 생물학적으로 어떤 관계가 있는지 알기가 어렵다. 본 연구에서는 유전자 발현량 데이터를 이미지 형태로 변환하여 예후 예측이 효과적으로 특징 선택 및 예후 예측을 수행할 수 있는 기법을 제안한다. 유전자들 사이의 생물학적 상호작용 관계를 유전자 발현량 데이터에 통합하기 위해 Node2Vec을 활용하였으며, 2차원 이미지로 표현된 발현량 데이터를 효과적으로 학습할 수 있도록 합성곱 신경망 모델을 사용하였다. 제안하는 모델의 성능은 이중 교차검증을 통해 평가되었고, 유전자 발현량 데이터를 그대로 이용하는 기계학습모델보다 우월한 예후 예측 정확도를 가지는 것이 확인되었다. Node2Vec을 이용한 유전자 발현량의 새로운 이미지 표현법은 특징 선택으로 인한 정보의 손실이 없어 예측 모델의 성능을 높일 수 있으며, 이러한 접근법이 개인 맞춤형 의학의 발전에 이바지할 것으로 기대한다.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 2004년도 제56차 추계 학술대회 연제집
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• pp.40.1-40.1
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• 2004

• 한국식물학회:학술대회논문집
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• 한국식물학회 1999년도 제13회 식물생명공학심포지움 New Approaches to Understand Gene Function in Plants and Application to Plant Biotechnology
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• pp.21-35
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• 1999

We have previously isolated OsMADS4 gene that is a member of the class B MADS box genes from rice. In this study, another member of the class B MADS box genes was isolated from rice flower by the yeast two-hybrid screening method using OsMADS4 as bait. RNA blot analyses revealed that the clone, OsMADS16, was expressed in the second and third whorls, whereas the OsMADS4 transcripts were present in the second, third, and fourth whorls. These expression patterns of the OsMADS16 and OsMADS4 genes are very similar with those of AP3 and PI, the class B genes of Arabidopsis, respectively. In the yeast two-hybrid system, OsMADS4 interacted only with OsMADS16 among several rice MADS genes investigated, suggesting that OsMADS4 and OsMADS16 function as a heterodimer in specifying sepal and petal identities. We have also isolated OsMADS6 gene using OsMADS1 as a probe. Both are members of the AGL2 MADS family. Various MADS genes that encode for protein-protein interaction partners of the OsMADS6 protein were isolated by the yeast two-hybrid screening method. A majority of these genes belong to the AGL2 family. Sequence Homology, expression pattern, and ectopic expression phenotypes indicated that one of the interaction partners, OsMADS14, appears to be homologous to API, the class A MADS gene of Arabidopsis.

• Genomics & Informatics
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• 제10권1호
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• Animal Bioscience
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• 제36권5호
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• pp.704-709
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• 2023

Objective: In tropical, subtropical and arid zones, heat stress is the main cause of productivity reduction in cattle. When climate stressors occur, animals become thermal adapted through differential expression of some genes, including heat shock proteins (HSP) family. The aim of this study was to determine levels of expression of HSP60, HSP70, and HSP90 genes in Simmental cattle raised in tropical environments of Mexico. Methods: In this study, expression of HSP60, HSP70, and HSP90 genes was analyzed in 116 Simmental cattle from three farms with tropical climate located in western Mexico. Animals were sampled twice a day, in the morning and noon. Gene expression was evaluated by quantitative polymerase chain reaction using probes marked with fluorescence. The MIXED procedure of SAS with repeated measures was used for all statistical analysis. Results: HSP60 gene expression differences were found for sex (p = 0.0349). HSP70 gene differences were detected for sampling hour (p = 0.0042), farm (p<0.0001), sex (p = 0.0476), and the interaction sampling hour×farm (p = 0.0002). Gene expression differences for HSP90 were observed for farm (p<0.0001) and year (p = 0.0521). HSP70 gene showed to be a better marker of heat stress than HSP60 and HSP90 genes. Conclusion: Expression of HSP70 gene in Simmental herds of the tropical region of western México was different during early morning and noon, but the expression of the HSP60 and HSP90 genes was similar. Identification of resilient animals to heat stress will be useful in the genetic improvement of the Simmental breed.