• Genomics & Informatics
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• v.9 no.2
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• pp.89-91
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• 2011

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.578-584
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• 2004

The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.551-558
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• 1998

In this paper, a job shop scheduling system is developed which can cope with the changes of shop floor status with flexibility. This system suggests near optimal sequence of operations by using Genetic Algorithm which considers alternative process plans. The Genetic Algorithm proposed in this paper has some characteristics. The mutation rate is differentiated in order to enhance the chance to escape a local optimum and to assure the global optimum. And it employs the double gene structure to easily make the modeling of the shop floor. Finally, the quality of its solution and the computational time are examined in comparison with the method of a Simulated Annealing.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.1
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• pp.31-38
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• 2019

We present an explicit numerical algorithm for surface reconstruction from unorganized points using the Gaussian filter. We construct a surface from unorganized points and solve the modified heat equation coupled with a fidelity term which keeps the given points. We apply the operator splitting method. First, instead of solving the diffusion term, we use the Gaussian filter which has the effect of diffusion. Next, we solve the fidelity term by using the fully implicit scheme. To investigate the proposed algorithm, we perform computational experiments and observe good results.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.253-256
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• 2001

Today's rapid development in the computer and network technology makes the environment which enables the companies to consider their decisions on the wide point of view and enables the software vendors to make the software packages to help these decisions. To make these software packages, many algorithms should be developed. The production and distribution planning problem belongs to those problems that industry manufacturers daily face in organizing their overall production plan. However, this combinatorial optimization problem can not be solved optimally in a reasonable time when large instances are considered. This legitimates the search for heuristic techniques. As one of these heuristic techniques, genetic algorithm has been considered in many researches. A standard genetic algorithm is a problem solving method that apply the rules of reproduction, gene crossover, and mutation to these pseudo-organisms so those organisms can Pass beneficial and survival-enhancing traits to new generation. This standard genetic algorithm should not be applied to this problem directly because when we represent the chromosome of this problem, there may exist high epitasis between genes. So in this paper, we proposed the hybrid genetic algorithm which turns out to better result than standard genetic algorithms

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.1
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• pp.3-7
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• 2005

The insulin-like growth factors (IGFs), their receptors, and their binding proteins play key roles in regulating cell proliferation and apoptosis. Insulin-like growth factor binding protein-3 (IGFBP3, OMIM #146732) is one of the proteins that bind to the IGFs. IGFBP3 is a modulator of IGF bioactivity, and direct growth inhibitor in the extravascular tissue compartment. We identified twenty-two novel single nucleotide polymorphisms (SNPs) in IGFBP3 gene in Korean cattle (Hanwoo, Bos taurus coreanae) by direct sequencing of full gene including -1,500 bp promoter region. Among the identified SNPs, five common SNPs were screened in 650 Korean cattle; one SNP in promoter (IGFBP3 G-854C), one in 5'UTR region (IGFBP3 G-100A), two in intron 1 (IGFBP3 G+421T, IGFBP3 T+1636A), and one in intron 2 (IGFBP3 C+3863A). The frequencies of each SNP were 0.357 (IGFBP3 G-854C), 0.472 (IGFBP3 G-100A), 0.418 (IGFBP3 G+421T), 0.363 (IGFBP3 T+1636A) and 0.226 (IGFBP3 C+3863A), respectively. Haplotypes and their frequencies were estimated by EM algorithm. Six haplotypes were constructed with five SNPs and linkage disequilibrium coefficients (|D'|) between SNP pairs were also calculated. The information on SNPs and haplotypes in IGFBP3 gene could be useful for genetic studies of this gene.

• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.810-814
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• 2003

For the phylogenetic analysis of procaryotes, 16S rRNA gene has been used. In spite of it's common use, so high conservative of 16S rRNA gene limited resolving power, hence other molecule was applied in this study and the result was compared with that of 16S rRNA. COG (Clusters of Orthologous of protein) algorithm revealed that three COGs were only detected in 42 procaryotes ; transcription elongation factor (COG0195), bacterial DNA primase (COG0358) and uridylate kinase (COG0528). Uridylate kinase gene was selected owing to the similarity and one single copy number in each genome. Phylogenetic tree of 16S rRNA gene and uridylate kinase showed similarities and differences. Uridylate kinase may help the problem of very high conservative of 16S rRNA gene in rhylogenetic analysis and it would help to access more accurate discrimination and phylogenetic analysis of bacteria.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2793-2800
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• 2015

In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that are absolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, using the R-based RankProd algorithm, and were able to identify a total of 158 differentially expressed genes (DEGs; 76 up- and 82 down-regulated) that are potentially involved in acquired resistance to gemcitabine. Indeed, the top 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in many cells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatory network, a gene co-expression network, and a protein-protein interaction network. The identified DEGs were functionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathway analyses. By systemic combinational analysis of the three molecular networks, we could condense the total number of DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20 up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patterns associated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1539-1546
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• 2016

Specific patterns of the hereditary breast and ovarian cancer (HBOC) syndrome are related to mutations in the BRCA1 gene. One hundred unrelated breast cancer patients were interviewed to obtain clinical symptoms and signs, pedigree and familial history of HBOC syndrome related cancer. Subsequently, data were calculated using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk prediction model. Patients with high score of BOADICEA were offered genetic testing. Eleven patients with high score of BOADICEA, 2 patients with low score of BOADICEA, 2 patient's family members and 15 controls underwent BRCA1 genetic testing. Mutation screening using PCR-HRM was carried out in 22 exons (41 amplicons) of BRCA1 gene. Sanger sequencing was subjected in all samples with aberrant graph. This study identified 10 variants in the BRCA1 gene, consisting of 6 missense mutations (c.1480C>A, c.2612C>T, c.2566T>C, c.3113A>G, c.3548 A>G, c.4837 A>G), 3 synonymous mutations (c.2082 C>T, c.2311 T>C and c.4308T>C) and one intronic mutation (c.134+35 G>T). All variants tend to be polymorphisms and unclassified variants. However, no known pathogenic mutations were found.