• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.153-159
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• 2009

In order to estimate release water from reservoirs located on ungaged watersheds, an algorithm was suggested based on hydrologic reservoir routing and real time calibrating watershed parameters. A prototype - simple computer program was developed to implement the algorithm with Genetic Algorithm technic. The program was applied to a mid-size reservoir and its ungauged watershed area using observed rainfall data, spillway gates operation data and reservoir water stage time series data under a existing storm event. The result shows that the algorithm and the prototype would be useful to simulate released water from reservoirs.

• Journal of the Korean Applied Science and Technology
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• v.40 no.5
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• pp.1010-1021
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• 2023

In this study, we would like to confirm the collagen recovery effect through cell experiments of the flavonoid apigenin and baicalein and propose results that can support the collagen recovery effect through comparative transcriptome analysis. As a result of the study, it was confirmed that apigenin and baicalein were effective in recovering type I collagen damaged by UV in the HS68 cell line, and that both substances resulted in changes in the expression of ECM-related genes. Not only did it show a common mechanism for regulating the ECM, but it also showed the results of changing different categories of genes, making it possible to predict that it would have various effects on cells.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.433-441
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• 2011

The optimization of a centrifugal compressor was conducted. The ANN (Artificial Neural Network) was adopted as an optimization algorithm, and it was learned and trained with the DOE (Design of Experiment). In the DOE, it was predicted the main effect and the interaction effect of design variables to the objective function. The ANN was improved in the optimization process using the GA (Genetic Algorithm). When any output at each generation was reached a standard level, it was re-calculated by the CFD (Computational Fluid Dynamics) and it was applied to develop a new ANN. After 6th generation, the prediction difference between ANN and CFD was less than 1%. A pareto of the efficiency versus the pressure ratio was obtained through the 21th generation. Using this method, the computational time for the optimization was equivalent to the time consumed by the gradient method, and the optimized results of multi-objective function were obtained.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.91-96
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• 2006

현대의학의 발전으로 많은 질병들의 치료율이 개선되고 있으나, 암은 여전히 낮은 치료율과 약제 내성 및 부작용으로 많은 환자들이 의학적 고통 뿐 아니라 정신적, 경제적 문제점들을 호소하고있다. 이와같은 문제점은 동일한 병리학적 특성을 가지는 종양이라도 사람마다 그 생물학적 특성이 다르며, 동일한 환자안에서도 종양의 시기에 따라 다양한 특성의 세포들이 공존하며 다양한 문제를 발생하는 tumor heterogeneity에서 기인하게된다. 다행히 최근의 분자생물학의 발전과 인간유전체연구들의 활성화로 이와같은 다양한 암의 특성과 환자들의 특성을 이해하는 연구 방법들의 개발로 환자의 특성에 맞는 항암제를 효율적으로 투여하는 맞춤치료를 향한 노력을 지속하고 있다. 이와같은 맞춤치료의 일환으로 약제의 환자에서의 반응과 부작용을 예측하고자 최신의 high-throughput 기법을 도입한 것이 Pharmacogenomics이다. 즉, 지금까지의 항암치료는 암의 종류에 따라 임상연구 결과에 근거한 항암제를 선택하고 있다. 그러나 앞서 설명한 것처럼 암의 특성과 환자 반응의 다양화로 실제 항암효과는 기대에 미치지 못하여 많은 수의 환자들이 치료에 내성을 보일 뿐 아니라 치명적인 부작용으로 새로운 문제에 대면하게 되었다. 따라서 각 항암제011 최대의 효과를 보이며 최소의 부작용을 나타내는 최선의 치료책을 선정하는 것이 중요한 과제이다. 이를 위해서 암환자의 치료 단계에서 정확한 진단 및 병기 설정, 생물학적 특성 이해 뿐 아니라, 치료 반응을 예측할 수 있는 생물학적 표지자를 찾고자 하는 노력의 결과로 현재 임상에 사용되는 몇 가지 종양표지자를 포함하여 다양한 유전자 칩들이 연구단계에 있다. 특히 다양한 생물학적 현상이 많은 유전자들의 변화에 의한다는 근거하여 약제의 효과와 부작용을 예측할 수 있는 표지자 발굴도 DNA chip 등의 high-throughput technology를 사용하여 그 특이도와 민감도가 향상된 표지자 발굴이 시도되고 있다. 아직은 시작단계이고 많은 검증이 필요하나 여러 가지 가능성의 증거들로 멀지않은 시기에 맞춤치료가 가능하리라 기대하며, 암 연구에 있어서 pharmacogenomics의 현황을 소개하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.550-557
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• 2007

We predict a context of various structures by using Bayesian Networks Algorithms, Three-Dimensional Structures Algorithms and Genetic Algorithms. However, these algorithms have unavoidable problems when providing a context-aware service in reality due to a lack of practicality and the delay of process time in real-time environment. As far as context-aware system for specific purpose is concerned, it is very hard to be sure about the accuracy and reliability of prediction. This paper focuses on reasoning and prediction technology which provides a stochastic mechanism for context information by incorporating various context information data. The objective of this paper is to provide optimum services to users by suggesting an intellectual reasoning and prediction based on hierarchical context information. Thus, we propose a design of user location prediction algorithm using sequential matching with n-size flexible window scheme by taking user's habit or behavior into consideration. This algorithm improves average 5.10% than traditional algorithms in the accuracy and reliability of prediction using the Flexible Window Scheme.

• Korean Journal of Biological Psychiatry
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• v.9 no.1
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• pp.25-33
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• 2002

Serotonin transporter(5-HTT) is one of the major action site of antidepressants in neuronal cells. According to the recent studies, it is known that the functional polymorphism in the promoter region of the 5-HTT gene(5-HTT linked polymorphism repetitive element in promoter region, 5-HTTLPR) is associated with antidepressant responsiveness, and the distributions of 5-HTTLPR is various among the different populations. Our preliminary study suggested that it is possible to measure the endophenotype of 5-HTTLPR genotype by examining the pharmacodynamic research of the 5-HTT in platelet membranes. However, there are limitations to predicting the antidepressant responsiveness only from the endophenotypic characteristics of 5-HTT gene promoter polymorphism, and therefore we propose to use the pharmacogenomic methods for overcoming these limitations. We found that the significant correlations existed among the genetic polymorphisms of biogenic amine transporters whose structure and characteristics are similar to the 5-HTT, and the predictable odds ratio of antidepressant responsiveness are increased significantly by combining the effect with other associated polymorphisms, compared to the effect of 5-HTT promoter polymorphism only. These results support the hypothesis that antidepressant treatment has to be individualized according to the genetic and ethnic background of depressed patients. It would be possible to develope the evaluation tools to predict the antidepressant responsiveness and its side effect profile, if scientists reveal the genes related to the action mechanism as well as the metabolism of antidepressants so as to discover the interaction of those genes and contribution of endogenotypes toward antidepressant responsiveness.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.192-192
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• 1994

본 논문에서는 cytochrome P450 LA1 유전자의 5'-upsteam 조절부위의 클로닝을 실시하였다. pUC19 vector에 연결시킨 3.4 Kb 크기의 Pstl DNA조각을 Sst1, Nco1 제한 효소로 자른 뒤, Exonuclease III 를 처리하여 약 200bp 씩의 차이를 갖는 여러 크기의 plasmid들을 얻었다. 이 plasmid 의 핵산서열을 알아보기 위해 dideoxy nucletide를 이용한 sequencing방법으로 그 핵산서열의 결정 실험을 시도하였다. 또한, 다환상 방향족 탄화수소 화합물에 반응성을 갖는 C57BL/6N 생쥐와 반응성을 갖지않는 DBA/2N 생쥐에 있어 phase II 대사 효소인UDP-glucuronosyltransferase 효소활성에 대한 3-methylcholanthrene의 영향을 알아보기 위해 C57BL/6N 생쥐와 DBA/2N 생쥐에 각각 다른 농도의 3-methylcholanthrene을 처리하거나 각기 다른 시간에 3-methylcholanthrene를 처리하였다. 그 결과 UDP-glucuronosyl-transferase의 mRNA가 3-methylcholanthrene양의 증가에 따라, 처치시간이 길어짐에 따라 증가되어지며 그 mRNA위 크기는 약 2.2Kb 정도임을 알았다. 이로부터 UDP-ghucuronosyltransferase 또한 cytochrome P45O와 함께 다환상 방향족 탄화수소 화합물 조절인자를 통한 조절을 받을 것이며 phase I phase II 약물 대사 효소가 조절상 밀접한 관련을 가짐을 예측할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.489-506
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• 2007

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated structures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. There are Ab Initio method, Similarity-based method, and Ensemble method for gene prediction method for eukaryotic genes. Each Method use various algorithms. This paper introduce how to predict genes using HMM(Hidden Markov Model) algorithm and present the process of gene prediction with well-known gene prediction programs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.434-437
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• 2004

본 논문에서는 유전자 알고리즘에 기반을 둔 자기구성 다항식 뉴럴네트워크(Self-Organizing Polynomial Neural Networks： SOPNN)의 새로운 구조를 제안하고, 포괄적인 설계 방법론을 토의한다. 기존의 자기구성 다항식 뉴럴 네트워크는 확장된 GMDH 방법에 기반을 두며, 네트워크의 성장과정을 통하여 각 충의 다항식 뉴런에서 고정된 노드 입력들의 수 뿐만 아니라 다항식 차수(1차, 2차, 그리고 수정된 2차식)를 이용하였다. 더구나, 그 방법은 학습을 통해 생성된 SOPNN이 최적 네트워크 구조를 가진다는 것을 보증하지 못한다. 그러나, 제안된 GA 기반 SOPNN은 그 구조를 구조적으로 더 최적화된 네트워크가 되도록 하고, 기존의 SOPNN보다 훨씬 더 유연하고, 선호된 뉴럴 네트워크가 되도록 한다. 구조적으로 더 최적화된 SOPNN을 생성하기 위해, SOPNN의 각 단계에서의 GA기반 설계 절차는 SOPNN내에서 이용할 수 있는 다음의 최적 파라미터들- 즉 입력변수의 수, 입력변수, 및 다항식 차수-을 가진 선호된 노드들의 선택으로 이끈다. 하중계수를 가진 합성성능지수가 그 모델의 근사화 및 일반화(예측) 능력 사이의 상호 균형을 얻기 위해 제안된다. 상세 설계 절차가 상세히 토의된다.