• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.08a
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• pp.103-127
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• 2001

Gene expression studies require statistical experimental designs and validation before laboratory confirmation. Various clustering approaches, such as hierarchical, Kmeans, SOM are commonly used for unsupervised learning in gene expression data. Several classification methods, such as gene voting, SVM, or discriminant analysis are used for supervised lerning, where well-defined response classification is possible. Estimating gene-condition interaction effects require advanced, computationally-intensive statistical approaches.

• Journal of Environmental Health Sciences
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• v.43 no.3
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• pp.223-232
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• 2017

Objectives: In response to increased interest in the safety of children's products, a risk management system is being prepared through exposure assessment of hazardous chemicals. To estimate exposure levels, risk assessors are using deterministic and probabilistic approaches to statistical methodology and a commercialized Monte Carlo simulation based on tools (MCTool) to efficiently support calculation of the probability density functions. This study was conducted to analyze and discuss the usage patterns and problems associated with the results of these two approaches and MCTools used in the case of probabilistic approaches by reviewing research reports related to exposure assessment for children's products. Methods: We collected six research reports on exposure and risk assessment of children's products and summarized the deterministic results and corresponding underlying distributions for exposure dose and concentration results estimated through deterministic and probabilistic approaches. We focused on mechanisms and differences in the MCTools used for decision making with probabilistic distributions to validate the simulation adequacy in detail. Results: The estimation results of exposure dose and concentration from the deterministic approaches were 0.19-3.98 times higher than the results from the probabilistic approach. For the probabilistic approach, the use of lognormal, Student's T, and Weibull distributions had the highest frequency as underlying distributions of the input parameters. However, we could not examine the reasons for the selection of each distribution because of the absence of test-statistics. In addition, there were some cases estimating the discrete probability distribution model as the underlying distribution for continuous variables, such as weight. To find the cause of abnormal simulations, we applied two MCTools used for all reports and described the improper usage routes of MCTools. Conclusions: For transparent and realistic exposure assessment, it is necessary to 1) establish standardized guidelines for the proper use of the two statistical approaches, including notes by MCTool and 2) consider the development of a new software tool with proper configurations and features specialized for risk assessment. Such guidelines and software will make exposure assessment more user-friendly, consistent, and rapid in the future.

• Journal of Biosystems Engineering
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• v.37 no.4
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• pp.258-264
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• 2012

Purpose: This review aims at introducing 3 modeling approaches classified into 3 categories based on the purpose (estimation or prediction), structure (linear or non-linear) and phase (steady-state or dynamic-state); 1) statistical approaches, 2) kinetic modeling and 3) mechanistic modeling. We hope that this review can be a useful guide in the model-based approach of calcium metabolism as well as illustrates an application of engineering tools in studying biosystems. Background: The meaning of biosystems has been expanded, including agricultural/food system as well as biological systems like genes, cells and metabolisms. This expansion has required a useful tool for assessing the biosystems and modeling has arisen as a method that satisfies the current inquiry. To suit for the flow of the era, examining the system which is a little bit far from the traditional biosystems may be interesting issue, which can enlarge our insights and provide new ideas for prospective biosystem-researches. Herein, calcium metabolic models reviewed as an example of application of modeling approaches into the biosystems. Review: Calcium is an essential nutrient widely involved in animal and human metabolism including bone mineralization and signaling pathways. For this reason, the calcium metabolic system has been studied in various research fields of academia and industries. To study calcium metabolism, model-based system analyses have been utilized according to the purpose, subject characteristics, metabolic sites of interest, and experimental design. Either individual metabolic pathways or a whole homeostasis has been modeled in a number of studies.

• Journal of Society of Preventive Korean Medicine
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• v.20 no.1
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• pp.99-110
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• 2016

In Korea and China, traditional medicine's holistic approaches, based on the views of whole-body and whole-person, have been applied to make the solution of health problem. However, these holistic approaches of traditional korea or chinese medicine have been limited in interpreting their theories in a view of modern scientific aspects of medicine. This limitation seems to be mainly due to the reductionism approaches of modern scientific medicine. Traditionally, science has taken a reductionism approach; dissecting biological systems into their constituent parts and studying them in isolation. However, systems biology based on omics technologies is providing a new thought and method for traditional medicine's research and interpretation. Systems biology uses integrity study as the characteristic and bioinformatic technology as the key method for connecting reductionism and holism. Therefore, it has much in common with the theory of traditional medicine. It was reviewed that how systems biology is applied to traditional medicine in Korea and China. Also it was suggested that more future researches on interpretation between traditional medicine and systems biology must be focused on personalized medicine since systems biology will have a major impact on future personalized therapeutic approaches.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.64-70
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• 2013

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• ETRI Journal
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• v.42 no.1
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• Structural Engineering and Mechanics
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• v.73 no.6
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• pp.699-714
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• 2020

Force based design (FBD) approach is prevalent in most of the national seismic design codes world over. Direct displacement based design (DDBD) and energy based design (EBD) approaches are relatively new methods of seismic design which claims to be more rational and predictive than the FBD. These three design approaches are conceptually distinct and imparts different strength, stiffness and ductility property to structural members for same plan configuration. In present study behavioural assessment of frame of six storey RC building designed using FBD, DDBD and EBD approaches has been performed. Lateral storey forces distribution, reinforcement design and results of nonlinear performance using static and dynamic methods have been compared. For the three approaches, considerable difference in lateral storey forces distribution and reinforcement design has been observed. Nonlinear pushover analysis and time history analysis results show that in FBD frame plastic deformation is concentrated in the lower storey, in EBD frame large plastic deformation is concentrated in the middle storeys though the inelastic hinges are well distributed over the height and, in DDBD frame plastic deformation is approximately uniform over the height. Overall the six storey frame designed using DDBD approach seems to be more rational than the other two methods.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.2
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• pp.37-44
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• 2017

This research is devoted to the development of methods general and standard methodological approaches and approbation those for the evaluation of economic potential and level of concentration of the regions of Kazakhstan. This paper presents the results of development of the authors on the selection and justification of the methodological approaches for quantitative evaluation of the economic potential (the degree of territorial differentiation of the profile) and concentration of regions. In this study, we used scientific methods: method of analysis the main trends of economic development, and method of evaluation of concentration of the region. Based on the analysis of foreign techniques developed and tested methodical approaches to the assessment of the economic potential (index and coefficient methods). Proposed methodological approaches to the assessment profile of the territory and developed a system of indicators, which includes an aggregated index of spatial concentration, which accurately reflects the concentration of production in the region. This study shows the results of the analysis of the potential regional disparities and trends of economic development of Kazakhstan. By using, the proposed methodology shows the possibility of their use; we calculated the indicators of integrated assessment of the economic potential and indicators of spatial concentration.