• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.283-285
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• 2004

마이크로 어레이 기술로 인해서 유전자의 발현 데이터를 대량으로 얻을 수 있게 되었다. 따라서 실험조건에 따른 유전자 발현 양상을 한눈에 볼 수 있게 되었고. 이를 기반으로 유전자간의 조절 관계를 예측할 수 있게 되었다. 또한 실험 이미지와 분석 파일들이 많아짐에 따라서 이러한 데이터를 효율적으로 관리하고, 저장하는 시스템이 필요하게 되었다. 이 두 가지 시스템을 통합함으로써 유전자 조절 네트워크 분석에 필요한 발현 데이터를 체계적으로 관리하고 손쉽게 얻을 수 있을 뿐만 아니라 분석 결과 또한 효율적으로 관리할 수 있다. 본 논문에서는 유전자 네트워크 분석 시스템과 마이크로 이미지 및 분석 데이터 관리 시스템을 통합한 시스템을 소개하고 각 시스템에서 제공하는 기능과 통합 시스템의 특징에 대해서 소개한다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.380-384
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• 2010

기후온난화, 국지성 호우 및 대규모 태풍으로 인한 피해가 증대되면서 사회 경제적 손실 또한 날로 증가하고 있어 재해로 인한 피해 발생가능성을 효율적으로 예측하는 모델을 통한 선제적 대응이 필요하다. 재난 재해의 위험성 분석 방법은 주로 확률 통계기법을 기반으로 하는 연구가 주류를 이루었으나, 본 논문에서는 포착된 현상의 데이터를 이용해 그 데이터를 지배하는 경험적 규칙성을 학습하고 획득하는데 다른 기법보다 탁월한 성능을 가진 신경망 모델을 적용하여 자연재해 피해예측 모델을 연구하였다. 1991년부터 2005년 사이에 우리나라에서 발생한 자연재해의 피해자료와 기상개황 자료를 이용하여 지역별 자연재해로 인한 피해를 예측하는 신경망 모델은 우리나라 232개 행정구역에 대하여 누적강우량과 최대풍속, 그리고 재해사상 발생 5일 이내의 선행강우량을 입력변수로 하고 총 피해액을 출력변수로 한다. 또한 학습을 통한 최적의 해를 찾기 위해 신경망의 매개변수 학습률, 모멘텀, 편의값을 유전자알고리즘으로 결정하여 학습을 수행 하였다.

• Computers and Concrete
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• v.24 no.3
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• pp.271-282
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• 2019

Concrete undergoes a series of thermo-based physio-chemical changes once exposed to elevated temperatures. Such changes adversely alter the composition of concrete and oftentimes lead to fire-induced explosive spalling. Spalling is a multidimensional, complex and most of all sophisticated phenomenon with the potential to cause significant damage to fire-exposed concrete structures. Despite past and recent research efforts, we continue to be short of a systematic methodology that is able of accurately assessing the tendency of concrete to spall under fire conditions. In order to bridge this knowledge gap, this study explores integrating novel artificial intelligence (AI) techniques; namely, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), together with traditional statistical analysis (multilinear regression (MLR)), to arrive at state-of-the-art procedures to predict occurrence of fire-induced spalling. Through a comprehensive datadriven examination of actual fire tests, this study demonstrates that AI techniques provide attractive tools capable of predicting fire-induced spalling phenomenon with high precision.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Microbiology and Biotechnology Letters
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• v.49 no.4
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• pp.478-484
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• 2021

Cellular resources including transcriptional and translational machineries in bacteria are limited, yet microorganisms depend upon them to maximize cellular fitness. Bacteria have evolved strategies for using resources economically. Regulatory networks for the gene expression system enable the cell to synthesize proteins only when necessary. At the same time, regulatory interactions enable the cell to limit losses when the system cannot make a cellular profit due to fake substrates. Also, the architecture of the gene expression flow can be advantageous for clustering functionally related products, thus resulting in effective interactions among molecules. In addition, cellular systems modulate the investment of proteomes, depending upon nutrient qualities, and fast-growing cells spend more resources on the synthesis of ribosomes, whereas nonribosomal proteins are synthesized in nutrient-limited conditions. A deeper understanding of cellular mechanisms underlying the optimal allocation of cellular resources can be used for biotechnological purposes, such as designing complex genetic circuits and constructing microbial cell factories.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• IMMUNE NETWORK
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• v.21 no.1
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• pp.6.1-6.11
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• 2021

Adenovirus was originally used as a vector for gene therapy. In recent years, with the development of the next-generation vectors with increased safety and high immunogenicity to transgene products, its utility as a vaccine vector has continued to increase. Adenovirus-based vaccines are currently being tested not only to prevent various infectious diseases but also to be applied as cancer vaccines. In this review, I discuss the innate and adaptive aspects of the immunological characteristics of adenovirus vectors and further examine the current status of advanced adenovirus-based vaccine development. Various methods that can overcome the limitations of currently used adenoviruses as vaccine vehicles are also discussed. Through this study, I hope that vaccine development using adenovirus vectors will be expedited and more successful.

• IMMUNE NETWORK
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• v.16 no.1
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• pp.44-51
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• 2016

Dendritic cells (DCs) are professional antigen presenting cells, and play an important role in the induction of antigen-specific adaptive immunity. However, some DC populations are involved in immune regulation and immune tolerance. These DC populations are believed to take part in the control of immune exaggeration and immune disorder, and maintain immune homeostasis in the body. Tolerogenic DCs (tolDCs) can be generated in vitro by genetic or pharmacological modification or by controlling the maturation stages of cytokine-derived DCs. These tolDCs have been investigated for the treatment of rheumatoid arthritis (RA) in experimental animal models. In the last decade, several in vitro and in vivo approaches have been translated into clinical trials. As of 2015, three tolDC trials for RA are on the list of ClinicalTrial.gov (www.clinicaltrials.gov). Other trials for RA are in progress and will be listed soon. In this review, we discuss the evolution of tolDC-based immunotherapy for RA and its limitations and future prospects.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.