• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.796-801
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• 2009

In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computation machines. It has been widely paid attention in diagnosing diseases or predicting the progress of diseases. A nomogram is constructed from a set of clinical data which contain various attributes such as symptoms, lab experiment results, therapy history, progress of diseases or identification of diseases. It is of importance to select effective ones from available attributes, sometimes along with parameters accompanying the attributes. This paper introduces a nomogram construction method that uses a naive Bayesian technique to construct a nomogram as well as a genetic algorithm to select effective attributes and parameters. The proposed method has been applied to the construction of a nomogram for a real clinical data set.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.59-65
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• 2019

There are various subtypes of cells in cancer tissues, but it is hard to confirm their composition experimentally. Here, we estimated the cell composition of each sample from gene expression data by using statistical machine learning approaches, two different regression models and investigated whether the cell composition was different between cancer and normal tissue. As a result, we found that CD8 T cell and Neutrophil were increased in oral cancer tissues compared to normal tissues. In addition, we applied t-SNE, which is one of the unsupervised learning, to verify whether normal tissue and oral cancer tissue can be clustered by the derived cell composition. Moreover, we showed that it is possible to predict oral cancer and normal tissue by several supervised classification algorithms. The study would help to improve the understanding of the immune cell infiltration at oral cancer.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.33-45
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• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.433-442
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• 2010

A control of chloride diffusion coefficient is very essential for service life of reinforced concrete (RC) structures exposed to chloride attack so that much studies have been focused on this work. The purpose of this study is to derive the intended diffusion coefficient which satisfies intended service life and propose a technique for optimum concrete mixture through genetic algorithm(GA). For this study, 30 data with mixture proportions and related diffusion coefficients are analyzed. Utilizing 27 data, fitness function for diffusion coefficient is obtained with variables of water to binder ratio(W/B), weight of cement, mineral admixture(slag, flay ash, and silica fume), sand, and coarse aggregate. 3 data are used for verification of the results from GA. Average error from fitness function is observed to 18.7% for 27 data for diffusion coefficient with 16.0% of coefficient of variance. For the verification using 3 data, a range of error for mixture proportions through GA is evaluated to 0.3~9.3% in 3 given diffusion coefficients. Assuming the durability design parameters like intended service life, cover depth, surface chloride content, and replacement ratio of mineral admixture, target diffusion coefficient, where exterior conditions like relative humidity(R.H.) and temperature, is derived and optimum design mixtures for concrete are proposed. In this paper, applicability of GA is attempted for durability mixture design and the proposed technique would be improved with enhancement of comprehensive data set including wider range of diffusion coefficients.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.639-647
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• 2009

From development of computer science, genetic algorithm has been applied to many fields for search like non-linear problem based on various variables and optimization process. Among others, in the data mining field, there are methods to select the best input variables for model accuracy and various predict models which were merged by using the genetic algorithm. In the meantime, to improve and preserve quality of the Hanwoo (Korean cattle) which is represented the agricultural industry in our country, we need to find out outstanding economical traits of Hanwoo in having specific genotype of single nucleotide polymorphism (SNP) which is inherited to next generation. According to, This research proposed the selecting method to find genotype of SNPs marker which affects economical traits of the Hanwoo by using the genetic algorithm. And we selected the best genotypes of the principal SNPs marker by applying to real data on Hanwoo genetic.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.802-808
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• 2009

Recently many researches have been presented to improve the clustering performance of gene expression data by incorporating Gene Ontology into the process of clustering. In particular, Kustra et al. showed higher performance improvement by exploiting Biological Process Ontology compared to the typical expression-based clustering. This paper extends the work of Kustra et al. by performing extensive experiments on the way of incorporating GO structures. To this end, we used three ontological distance measures (Lin's, Resnik's, Jiang's) and three GO structures (BP, CC, MF) for the yeast expression data. From all test cases, We found that clustering performances were remarkably improved by incorporating GO; especially, Resnik's distance measure based on Biological Process Ontology was the best.

• Journal of Marine Life Science
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• v.5 no.1
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• pp.17-24
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• 2020

We have identified a heat shock protein 70 gene from freshwater snail, Semisulcospira coreana. The freshwater snail HSP70 gene encode a polypeptide of 639 amino acids. Based on bioinformatic sequence characterization, HSP70 gene possessed three classical signature motifs and other conserved residues essential for their functionality. The phylogenetic analysis showed that S. coreana HSP70 had closet relationship with that of golden apple snails, Pomacea canaliculata. The HSP70 mRNA level was significantly up-regulated in response to thermal and salinity challenges. These results are in agreement with the results of other species, indicating that S. coreana HSP70 used be a potential molecular marker in response to external stressors and the regulatory process related to the HSP70 transcriptional response can be highly conserved among species.

• Clinical and Experimental Pediatrics
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• v.46 no.4
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• pp.335-339
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• 2003

Purpose : Surfactant protein A(SP-A) is involved in surfactant physiology and structure, and plays a major role in innate host defense and inflammatory processes in the lung. Steroid therapy is widely used for mothers who threaten to deliver prematurely and also used commonly in the management of preterm infants with chronic lung disease. Two SP-A genes(SP-A1, SP-A2) and several alleles have been characterized for each SP-A gene in human. Preliminary evidence indicates that differences may exist among alleles in response to Dexamethasone(Dexa) and that the SP-A 3'UTR plays a role in this process. We studied whether 3'UTR-mediated differences exist among the most frequently found SP-A alleles in response to Dexa. Methods : Constructs containing the 3'UTR from eight different SP-A alleles were made using luciferase as a the reporter gene. These constructs were driven by the SV40 promotor and were transfected along with a transfection control vector in H441 cells that express SP-A. The activity of the reporter gene in the presence or absence of Dexa(100 nM) treatment was measured. All the experiments for the eight SP-A alleles studied, were performed in triplicate and repeated five times. The results were normalized to the transfection control. Results : Expression of alleles of 6A3, 6A, 1A were significantly decreased in response to Dexa. Conclusion : Three UTR mediated differences exist among human SP-A variants both in the basal expression and in response to Dexa. These genotype-dependent differences may point to a need for a careful consideration of individual use of steroid treatment in the prematurely born infant.