• Journal of Korean Society of Forest Science
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• v.98 no.5
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• pp.531-538
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• 2009

We used enzyme electrophoresis to evaluate genetic diversity in five populations of endemic ash, Fraxinus chiisanensis in Korea. Of 15 putative allozyme loci examined 26.7% were polymorphic and expected heterozygosity for the species was low (0.082). Within the range, population were highly differentiated ($F_{ST}$=0.356) and little genetic variation was explained by geography. The pattern of distribution of variation showed low genetic variation within populations and pronounced divergence among populations, which was consistent with the prediction for the effects of limited gene flow and local genetic erosion. Although the frequencies of male plants were dominant ranging from 79.3% to 89.4%, most mating events seems to be inevitable mating between relatives in small populations based on heterozygote deficiency of this species. Small effective population size and the limited dispersal contributed to the low rates of gene flow within as well as between populations.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.2
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• pp.515-519
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• 2005

We hypothesized that the IL10 gene is important candidate in the development of Bell's palsy and specific genotypic and allelic variations should be associated with Bell's palsy in the Korean population. In this study, we assessed the SNP (single-nucleotide polymorphism) of IL10 in patients with Bell's palsy. 62 patients with Bell's palsy were selected from the subjects who visited for the Bell's palsy service of the department of acupuncture & moxibustion, college of Oriental Medicine, Daegu Haany University from May 2002 to May 2003. Pyrosequencing was performed for genetic analyses. There was no statistically significant genotypic distribution difference between control and Bell's palsy group And there was not statistically significant allelic frequency difference between control and Bell's palsy group. In this study the IL10 genotypemight not be the risk factor of Bell's palsy patients in Korean. studies will be necessary for the exact genetic markers. Establishment of more systemic approach and high quality of prospective cohorts will be necessary for the good prediction of genetic markers.

• Genomics & Informatics
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• v.20 no.2
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• pp.24.1-24.8
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• 2022

Hypomyelinating leukodystrophy type 2 (HLD2), is an inherited genetic disease of the central nervous system caused by recessive mutations in the gap junction protein gamma 2 (GJC2/GJA12). HLD2 is characterized by nystagmus, developmental delay, motor impairments, ataxia, severe speech problem, and hypomyelination in the brain. The GJC2 sequence encodes connexin 47 protein (Cx47). Connexins are a group of membrane proteins that oligomerize to construct gap junctions protein. In the present study, a novel missense mutation gene c.760G>A (p.Val254Met) was identified in a patient with HLD2 by performing whole exome sequencing. Following the discovery of the new mutation in the proband, we used Sanger sequencing to analyze his affected sibling and parents. Sanger sequencing verified homozygosity of the mutation in the proband and his affected sibling. The autosomal recessive inheritance pattern was confirmed since Sanger sequencing revealed both healthy parents were heterozygous for the mutation. PolyPhen2, SIFT, PROVEAN, and CADD were used to evaluate the function prediction scores of detected mutations. Cx47 is essential for oligodendrocyte function, including adequate myelination and myelin maintenance in humans. Novel mutation p.Val254Met is located in the second extracellular domain of Cx47, both extracellular loops are highly conserved and probably induce intramolecular disulfide interactions. This novel mutation in the Cx47 gene causes oligodendrocyte dysfunction and HLD2 disorder.

• The Korea Journal of Herbology
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• v.38 no.1
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.

• Journal of Korean Dental Science
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• v.16 no.1
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• pp.35-46
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• 2023

Purpose: Culture-based methods for microbiological diagnosis and antibiotic susceptibility tests have limitations in the management of orofacial infections. We aimed to profile pus microbiota and identify antibiotic resistance genes (ARGs) using a culture-independent approach. Materials and Methods: Genomic DNA samples extracted from the pus specimens of two patients with orofacial abscesses were subjected to shotgun sequencing on the NovaSeq system. Taxonomic profiling and prediction of ARGs were performed directly from the metagenomic raw reads. Result: Taxonomic profiling revealed obligate anaerobic polymicrobial communities associated with infections of odontogenic origins: the microbial community of Patient 1 consisted of one predominant species (Prevotella oris 74.6%) with 27 minor species, while the sample from Patient 2 contained 3 abundant species (Porphyromonas endodontalis 33.0%; P. oris 31.6%; and Prevotella koreensis 13.4%) with five minor species. A total of 150 and 136 putative ARGs were predicted in the metagenome of each pus sample. The coverage of most predicted ARGs was less than 10%, and only the CfxA2 gene identified in Patient 1 was covered 100%. ARG analysis of the seven assembled genome/metagenome datasets of P. oris revealed that strain C735 carried the CfxA2 gene. Conclusion: A metagenomics-based approach is useful to profile predominantly anaerobic polymicrobial communities but needs further verification for reliable ARG detection.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.231-242
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• 2023

This paper represents a comparative study in which Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), and multiple linear regressions (MLR) were utilized to derive new equations for the prediction of time-dependent bearing capacity of pile foundations driven in cohesive soil, technically called pile set-up. This term means that many piles which are installed in cohesive soil experience a noticeable increase in bearing capacity after a specific time. Results of researches indicate that side resistance encounters more increase than toe resistance. The main reason leading to pile setup in saturated soil has been found to be the dissipation of excess pore water pressure generated in the process of pile installation, while in unsaturated conditions aging is the major justification. In this study, a comprehensive dataset containing information about 169 test piles was obtained from literature reviews used to develop the models. to prepare the data for further developments using intelligent algorithms, Data mining techniques were performed as a fundamental stage of the study. To verify the models, the data were randomly divided into training and testing datasets. The most striking difference between this study and the previous researches is that the dataset used in this study includes different piles driven in soil with varied geotechnical characterization; therefore, the proposed equations are more generalizable. According to the evaluation criteria, GEP was found to be the most effective method to predict set-up among the other approaches developed earlier for the pertinent research.

• Genomics & Informatics
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• v.21 no.1
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• pp.6.1-6.8
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• 2023

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.61-62
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• 2022

알츠하이머성 치매는 현존하는 치료법이 없어 경도인지장애 단계에서의 예방이 중요하다. 지금까지의 알츠하이머 연구는 대부분이 뇌영상 마커와 뇌척수액 마커에 집중되어 있었으며, 경도 인지 장애 단계에서의 탐색은 더욱 적었다. 이러한 점에서 혈액 유전자 발현을 이용한 경도 인지장애 단계 예측은 인지 능력에 따른 관련 유전자 식별과 접근 가능한 진단 및 치료 바이오 마커 탐색에 기여할 수 있다. 그러나 유전자 발현 데이터의 경우 환자 수에 비해 높은 차원을 가지기 때문에 과적합을 막고 질병 관련 유전자를 식별하기 위해서는 데이터에서의 의미 있는 차원만을 뽑아내는 차원 축소가 선행되야 한다. 본 연구는 유전자 발현데이터에서의 인지장애 분류를 위해 차원 축소기법과 신경망을 적용하여 인지 장애 정도를 예측하였다. 그 결과, Lasso 이용 차원축소와 신경망을 이용하여 97%의 정확도로 정상과 조기 경도 인지장애, 후기 경도 인지장애 환자를 분류 할 수 있었으며, 더 적은 차원에서도 분류가 가능했다. 이는 혈액 유전자 발현을 이용해 경도 인지장애 단계를 예측한 첫 번째 연구이며, 인지능력 저하에 따른 혈액 유전자 발현의 연관성을 확인하고 향후 조기 진단, 치료 표적 탐색에 기여한다.

• Animal Bioscience
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• v.37 no.3
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• pp.451-460
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• 2024

Objective: The objective is to identify genomic regions and candidate genes associated with age to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) in Yorkshire pig. Methods: This study used a total of 104,380 records and 11,854 single nucleotide polymorphism (SNP) data obtained from Illumina porcine 60K chip. The estimated genomic breeding values (GEBVs) and SNP effects were estimated by single-step genomic best linear unbiased prediction (ssGBLUP). Results: The heritabilities of AGE, ADG, BF, and EMA were 0.50, 0.49, 0.49, and 0.23, respectively. We identified significant SNP markers surpassing the Bonferroni correction threshold (1.68×10^-6), with a total of 9 markers associated with both AGE and ADG, and 4 markers associated with BF and EMA. Genome-wide association study (GWAS) analyses revealed notable chromosomal regions linked to AGE and ADG on Sus scrofa chromosome (SSC) 1, 6, 8, and 16; BF on SSC 2, 5, and 8; and EMA on SSC 1. Additionally, we observed strong linkage disequilibrium on SSC 1. Finally, we performed enrichment analyses using gene ontology and Kyoto encyclopedia of genes and genomes (KEGG), which revealed significant enrichments in eight biological processes, one cellular component, one molecular function, and one KEGG pathway. Conclusion: The identified SNP markers for productive traits are expected to provide valuable information for genetic improvement as an understanding of their expression.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.