• Journal of Pharmaceutical Investigation
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• v.38 no.6
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• pp.365-372
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• 2008

The aim of this study was to investigate the frequency of the SNPs on MDR1 exon 12, 21 and 26 in Korean population and to analyze haplotype frequency on MDR1 exon 12, 21 and 26 in Korean population. A total of 426 healthy subjects was genotyped for MDR1, using polymerase chain reaction-based diagnostic tests. Haplotype was statistically inferred using an algorithm based on the expectation-maximization (EM). MDR1 C1236T genotyping revealed that the frequency for homozygous wild-type (C/C), heterozygous (C/T) and for homozygous mutant-type (T/T) was 20.19%, 46.48% and 33.33%, respectively. MDR1 G2677T/A genotyping revealed that the frequency for homozygous G/G, heterozygous G/T, homozygous T/T, heterozygous G/A, heterozygous T/A and for homozygous A/A type was 30.75%, 42.26%, 9.86%, 7.51 %, 7.04% and 2.58%, respectively. MDR1 C3435T genotyping revealed that the frequency for homozygous wild-type (C/C), heterozygous (C/T) and for homozygous mutant-type (T/T) was 38.73%, 50.24% and 11.03%, respectively. Twelve haplotypes were observed. Of the three major haplotypes identified (CGC, TTT and TGC), the CGC haplotype were mainly predominant in the Korean populations and accounted for 29.96% of total haplotype in Korean.

• Journal of Microbiology and Biotechnology
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• v.32 no.2
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• pp.149-159
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• 2022

Cancers of the lung and liver are the top 10 leading causes of cancer death worldwide. Thus, it is essential to identify the genes specifically expressed in these two cancer types to develop new therapeutics. Although many messenger RNA (mRNA) sequencing data related to these cancer cells are available due to the advancement of next-generation sequencing (NGS) technologies, optimized data processing methods need to be developed to identify the novel cancer-specific genes. Here, we conducted an analytical comparison between Bowtie2, a Burrows-Wheeler transform-based alignment tool, and Kallisto, which adopts pseudo alignment based on a transcriptome de Bruijn graph using mRNA sequencing data on normal cells and lung/liver cancer tissues. Before using cancer data, simulated mRNA sequencing reads were generated, and the high Transcripts Per Million (TPM) values were compared. mRNA sequencing reads data on lung/liver cancer cells were also extracted and quantified. While Kallisto could directly give the output in TPM values, Bowtie2 provided the counts. Thus, TPM values were calculated by processing the Sequence Alignment Map (SAM) file in R using package Rsubread and subsequently in python. The analysis of the simulated sequencing data revealed that Kallisto could detect more transcripts and had a higher overlap over Bowtie2. The evaluation of these two data processing methods using the known lung cancer biomarkers concludes that in standard settings without any dedicated quality control, Kallisto is more effective at producing faster and more accurate results than Bowtie2. Such conclusions were also drawn and confirmed with the known biomarkers specific to liver cancer.

• Journal of Nutrition and Health
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• v.55 no.6
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• pp.601-616
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• 2022

In the era of the fourth industrial revolution technology, the inclusion of personalized nutrition for healthcare (PNH), when establishing a healthcare platform to prevent chronic diseases such as obesity, diabetes, cerebrovascular and cardiovascular disease, pulmonary disease, and inflammatory diseases, enhances the national competitiveness of global healthcare markets. Furthermore, since the government experienced COVID-19 and the population dead cross in 2020, as well as numerous health problems due to an increasing super-aged Korean society, there is an urgent need to secure, develop, and utilize PNH-related technologies. Three conditions are essential for the development of PNH technologies. These include the establishment of causality between obesity genome (genotype) and prevalence (phenotype) in Koreans, validation of clinical intervention research, and securing PNH-utilization technology (i.e., algorithm development, artificial intelligence-based platform, direct-to-customer [DTC]-based PNH, etc.). Therefore, a national control tower is required to establish appropriate PNH infrastructure (basic and clinical research, cultivation of PNH-related experts, etc.). The post-corona era will be aggressive in sharing data knowledge and developing related technologies, and Korea needs to actively participate in the large-scale global healthcare markets. This review provides the importance of scientific evidence based on a huge dataset, which is the primary prerequisite for the DTC obesity gene-based PNH technologies to be competitive in the healthcare market. Furthermore, based on comparing domestic and internationally approved DTC obese genes and the current status of Korean obesity genome-based PNH research, we intend to provide a direction to PNH planners (individuals and industries) for establishing scientific PNH guidelines for the prevention of obesity.

• Journal of Life Science
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• v.30 no.9
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• pp.813-818
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• 2020

The archaeal clusters of orthologous genes (arCOG) algorithm, which identifies common genes among archaebacterial genomes, was used to identify conservative genes among 168 archaebacterial strains. The numbers of conserved orthologs were 14, 10, 9, and 8 arCOGs in 168, 167, 166, and 165 strains, respectively. Among 41 conserved arCOGs, 13 were related to function J (translation, ribosomal structure, and biogenesis), and 10 were related to function L (replication, recombination, and repair). Among the 14 conserved arCOGs in all 168 strains, 6 arCOGs of tRNA synthetase comprised the highest proportion. Of the remaining 8 arCOGs, 2 are involved in reactions with ribosomes, 2 for tRNA synthesis, 2 for DNA replication, and 2 for transcription. These results showed the importance of protein expression in archaea. For the classes or orders having 3 or more members, genomic analysis was performed by averaging the distance values of the conservative arCOGs. Classes Archaeoglobi and Thermoplasmata of the phylum Euryarchaeota showed the lowest and the highest average of distance value, respectively. This study can provides data necessary for basic scientific research and the development of antibacterial agents and tumor control.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.3
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• pp.229-236
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• 2020

Sixty-five molds isolated from clinical specimens were included in this study. All the isolates were molds that could be identified morphologically, strains that are difficult to identify because of morphological similarities, and strains that require species-level identification. PCR and direct sequencing were performed to target the internal transcribed spacer (ITS) region, the D1/D2 region, and the β-tubulin gene. Comparative sequence analysis using the GenBank database was performed using the basic local alignment search tool (BLAST) algorithm. The fungi identified morphologically to the genus level were 67%. Sequencing analysis was performed on 62 genera and species level of the 65 strains. Discrepancies were 14 (21.5%) of the 65 strains between the results of phenotypic and molecular identification. B. dermatitidis, T. marneffei, and G. argillacea were identified for the first time in Korea using the DNA sequencing method. Morphological identification is a very useful method in terms of the reporting time and costs in cases of frequently isolated and rapid growth, such as Aspergillus. When molecular methods are employed, the cost and clinical significance should be considered. On the other hand, the molecular identification of molds can provide fast and accurate results.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10577-10583
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• 2015

Background: Triple-negative breast cancer (TNBC), characterized by the lack of expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor-2, is typically associated with a poor prognosis. The majority of TNBCs show the expression of basal markers on gene expression profiling and most authors accept TNBC as basal-like (BL) breast cancer. However, a smaller fraction lacks a BL phenotype despite being TNBC. The literature is silent on non-basal-like (NBL) type of TNBC. The present study was aimed at defining behavioral differences between BL and NBL phenotypes. Objectives: i) Identify the TNBCs and categorize them into BL and NBL breast cancer. ii) Examine the behavioral differences between two subtypes. iii) Observe the pattern of treatment failure among TNBCs. Materials and Methods: All TNBC cases during January 2009-December 2010 were retrieved. The subjects fitting the inclusion criteria of study were differentiated into BL and NBL phenotypes using surrogate immunohistochemistry with three basal markers $34{\beta}E12$, c-Kit and EGFR as per the algorithm defined by Nielsen et al. The detailed data of subjects were collated from clinical records. The comparison of clinicopathological features between two subgroups was done using statistical analyses. The pattern of treatment failure along with its association with prognostic factors was assessed. Results: TNBC constituted 18% of breast cancer cases considered in the study. The BL and NBL subtypes accounted for 81% and 19% respectively of the TNBC group. No statistically significant association was seen between prognostic parameters and two phenotypes. Among patients with treatment failure, 19% were with BL and 15% were with NBL phenotype. The mean disease free survival (DFS) in groups BL and NBL was 30.0 and 37.9 months respectively, while mean overall survival (OS) was 31.93 and 38.5 months respectively. Treatment failure was significantly associated with stage (p=.023) among prognostic factors. Conclusions: Disease stage at presentation is an important prognostic factor influencing the treatment failure and survival among TNBCs. Increasing tumor size is related to lymph node positivity. BL tumors have a more aggressive clinical course than that of NBL as shown by shorter DFS and OS, despite having no statistically significant difference between prognostic parameters. New therapeutic alternatives should be explored for patients with this subtype of breast cancer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.903-913
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• 2015

The Korea Agency of Education, Promotion and Information Service in Food, Agriculture, Forestry and Fisheries launched a public data portal service in January 2015. The service provides customized information for consumers through an agri-food recommendation system built-in portal service. The recommendation system has fallowing characteristics. First, the system can increase recommendation accuracy by using a wide variety of agri-food related data, including SNS opinion mining, consumer's purchase data, climate data, and wholesale price data. Second, the system uses segmentation method based on consumer's lifestyle and megatrends factors to overcome the cold start problem. Third, the system recommends agri-foods to users reflecting various preference contextual factors by using recommendation algorithm, dirichlet-multinomial distribution. In addition, the system provides diverse information related to recommended agri-foods to increase interest in agri-food of service users.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1809-1815
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• 2019

Objective: Copy number variations (CNVs) are a major source of genetic diversity complementary to single nucleotide polymorphism (SNP) in animals. The aim of the study was to perform a comprehensive genomic analysis of CNVs based on high density whole-genome SNP markers in Chinese Dongxiang spotted pigs. Methods: We used customized Affymetrix Axiom Pig1.4M array plates containing 1.4 million SNPs and the PennCNV algorithm to identify porcine CNVs on autosomes in Chinese Dongxiang spotted pigs. Then, the next generation sequence data was used to confirm the detected CNVs. Next, functional analysis was performed for gene contents in copy number variation regions (CNVRs). In addition, we compared the identified CNVRs with those reported ones and quantitative trait loci (QTL) in the pig QTL database. Results: We identified 871 putative CNVs belonging to 2,221 CNVRs on 17 autosomes. We further discarded CNVRs that were detected only in one individual, leaving us 166 CNVRs in total. The 166 CNVRs ranged from 2.89 kb to 617.53 kb with a mean value of 93.65 kb and a genome coverage of 15.55 Mb, corresponding to 0.58% of the pig genome. A total of 119 (71.69%) of the identified CNVRs were confirmed by next generation sequence data. Moreover, functional annotation showed that these CNVRs are involved in a variety of molecular functions. More than half (56.63%) of the CNVRs (n = 94) have been reported in previous studies, while 72 CNVRs are reported for the first time. In addition, 162 (97.59%) CNVRs were found to overlap with 2,765 previously reported QTLs affecting 378 phenotypic traits. Conclusion: The findings improve the catalog of pig CNVs and provide insights and novel molecular markers for further genetic analyses of Chinese indigenous pigs.

• 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.

• Journal of Life Science
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• v.32 no.6
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• pp.463-467
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• 2022

As a result of applying the COG (Cluster of Orthologous Groups of Protein) algorithm to 1,309 species to confirm the conserved genes of prokaryotes, ribosomal protein S11 (COG0100) was identified. The numbers of conservative genes were 2, 5, 5, and 6 in 1,308, 1,307, 1,306, and 1,305 species, respectively. Twenty-nine genes were conserved in over 1,302 species, and they encoded 23 ribosomal proteins, 3 tRNA synthetases, 2 translation factors, and 1 RNA polymerase subunit. Most of them were related to protein production, suggesting the importance of protein expression in prokaryotes. The highest conservative COG was COG0048 (ribosomal protein S12) among the 29 COGs. The 29 conserved genes usually have one protein for each prokaryote. COG0090 (ribosomal protein L2) had not only the lowest conservation value but also the largest standard deviation of phylogenetic distance value. As COG0090 is not only a member of the ribosome, but also a regulator of replication and transcription, it could be inferred that prokaryotes have large variations in COG0090 to survive in various environments. This study could provide data necessary for basic science, tumor control, and development of antibacterial agents.