• Journal of the Korean Physical Society
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• v.73 no.9
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• pp.1385-1392
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• 2018

A network with coupled biological neurons provides various forms of collective synchronous dynamics. Such phase-locking dynamics states resemble eigenvectors in a linear coupling system in that the forms are determined by the symmetry of the coupling strengths. However, the states behave as attractors in a nonlinear dynamics system. We here study the collective synchronous dynamics in a neural system by using a novel theory. We exhibit how the period and the stability of individual phase-locking dynamics states are determined by the characteristics of synaptic couplings. We find that, contrary to common sense, the firing rate of a synchronized state decreases with increasing synaptic coupling strength.

• Korean Journal of Biological Psychiatry
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• v.12 no.2
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• pp.165-172
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• 2005

Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

• Journal of Korea Technology Innovation Society
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• v.15 no.2
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• pp.369-399
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• 2012

Purpose: Health technology research & development is an important area to leading future. This study examined the current trends for 'oncogene' based on the research subject network to deduce a research front. Method: Papers were extracted from PubMed database using MeSH term for studies on 'oncogenes' and further categorized as papers published by Korean. Keywords were collected from all of articles. Research subject network was generated by keywords. Research subject network was analyzed by weighted degree centrality based social network analysis and transition of research subjects was analyzed by the time series. Results: On 'oncogenes', 'Genes, ras', 'Apoptosis', 'Signal Transduction' had a high degree centrality and currently 'Antineoplastic Agents', 'Prognosis', and 'Tumor Markers, Biological' were widely conducted. Conclusion: Consistency of research trend pattern was found by analyzing oncogene network with compromised to international vs. domestic trends. Analyzing keyword networks in various subject area, those will allow us to predict the research progress and propose evidence of research & developmental strategy.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.36 no.1
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• pp.50-63
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• 2023

Objectives : This study used a network pharmacology approach to explore the active compounds and therapeutic mechanisms of Yijin-tang on Meniere's disease. Methods : The active compounds of Yijin-tang were screened via the TCMSP database and their target proteins were screened via the STITCH database. The GeneCard was used to establish the Meniere's disease-related genes. The intersection targets were obtained through Venny 2.1.0. The related protein interaction network was constructed with the STRING database, and topology analysis was performed through CytoNCA. GO biological function analysis and KEGG enrichment analysis for core targets were performed through the ClueGO. Results : Network analysis identified 126 compounds in five herbal medicines of Yijin-tang. Among them, 15 compounds(naringenin, beta-sitosterol, stigmasterol, baicalein, baicalin, calycosin, dihydrocapsaicin, formononetin, glabridin, isorhamnetin, kaempferol, mairin, quercetin, sitosterol, nobiletin) were the key chemicals. The target proteins were 119, and 7 proteins(TNF, CASP9, PARP1, CCL2, CFTR, NOS2, NOS1) were linked to Meniere's disease-related genes. Core genes in this network were TNF, CASP9, and NOS2. GO/KEGG pathway analysis results indicate that these targets are primarily involved in regulating biological processes, such as excitotoxicity, oxidative stress, and apoptosis. Conclusion : Pharmacological network analysis can help to explain the applicability of Yijin-tang on Meniere's disease.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.25-27
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• 2009

In this paper, a sensor network system for providing intelligent home network services is suggested. It steadily collects biological data of resident people and automatically detects emergency situations LEID(Lighting Embedded Information Device) system are the most essential component of the sensor network. They embed sensor network technology into lightening devices which are indispensable most living spaces. To verify practicality of the proposed intelligent home network service system, a prototypical system is realized in the Smart Home Industrialization Support Center at Kookmin University, and is tested within many practical circumstances.

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• Journal of Animal Reproduction and Biotechnology
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• v.37 no.4
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• pp.255-265
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• 2022

The cellular communication network factor (CCN) family proteins regulate many biological events such as angiogenesis, tumor growth, placentation, implantation, and embryogenesis. The expression and function of CCN1, CCN2, and CCN3 at the maternal-conceptus interface are established in humans and rodents, but little is known about the role of CCN4 to CCN6 in the reproductive organs in any other species. Several studies in transcriptome analysis in pigs have shown that the expression of CCN4 and CCN6 increases in the endometrium during early pregnancy. However, their expression, regulation, and function in the endometrium throughout the estrous cycle and pregnancy have not been fully understood in pigs. Thus, we determined the expression, localization, and regulation of CCN4 and CCN6 during the estrous cycle and at the maternal-conceptus interface in pigs. We found that the levels of CCN4, but not CCN6, changed during the estrous cycle. The levels of CCN4 were greater during mid- to late pregnancy than in the early stage, and the levels of CCN6 were greatest on Day 15 of pregnancy. CCN4 and CCN6 were detected in conceptus tissues during early pregnancy and in chorioallantoic tissues during the later stage of pregnancy. CCN4 mRNA was mainly localized to epithelial cells, CCN6 mRNAs to epithelial and stromal cells in the endometrium. In endometrial explant cultures, CCN4 expression was increased by progesterone, and CCN6 expression by interferon-𝛾. These results suggest that CCN4 and CCN6 may play roles in the establishment and maintenance of pregnancy by regulating the endometrial epithelial cell functions in pigs.

• Journal of Environmental Science International
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• v.33 no.4
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• pp.235-247
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• 2024

A total of 64 individuals of Acanthogobius flavimanus, which inhabit the coast of Korea, were collected from 8 regions from July to August 2023. A haplotype network and a phylogenetic tree were created. The genomic DNA of the target fish species was compared and analyzed with the genomic DNA of four regions in Japan downloaded from the National Center for Biotechnology Information (NCBI). In the haplotype network of Acanthogoboius flavimanus, Eocheong-do (EC) and Goseong (MAJ) exhibited low genetic similarity with other regions in Korea and Japan. The Phylogenetic tree showed that the population of MAJ exhibited differences in genetic structure compared to populations in other regions of Korea and Japan, indicating a distant relationship. Most marine organisms are known to migrate and spread via ocean currents, which is the most crucial factor promoting gene flow through larvae between populations. The haplotype of Acanthogobius flavimanus in MAJ differs from the haplotypes in Korea and Japan. The population in MAJ is believed to have limited genetic exchange due to the North Korea Cold Currents. We identified haplotype patterns based on the geographical distribution of Acanthogobius flavimanus off the coast of Korea and inferred that ocean currents have some influence on genetic distances.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5325-5330
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• 2014

Background: Chronic myelocytic leukemia is a disease that threatens both adults and children. Great progress has been achieved in treatment but protein-protein interaction networks underlining chronic myelocytic leukemia are less known. Objective: To develop a protein-protein interaction network for chronic myelocytic leukemia based on gene expression and to predict biological pathways underlying molecular complexes in the network. Materials and Methods: Genes involved in chronic myelocytic leukemia were selected from OMIM database. Literature mining was performed by Agilent Literature Search plugin and a protein-protein interaction network of chronic myelocytic leukemia was established by Cytoscape. The molecular complexes in the network were detected by Clusterviz plugin and pathway enrichment of molecular complexes were performed by DAVID online. Results and Discussion: There are seventy-nine chronic myelocytic leukemia genes in the Mendelian Inheritance In Man Database. The protein-protein interaction network of chronic myelocytic leukemia contained 638 nodes, 1830 edges and perhaps 5 molecular complexes. Among them, complex 1 is involved in pathways that are related to cytokine secretion, cytokine-receptor binding, cytokine receptor signaling, while complex 3 is related to biological behavior of tumors which can provide the bioinformatic foundation for further understanding the mechanisms of chronic myelocytic leukemia.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.4
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• pp.508-518
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• 2019

Objective: This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population. Methods: Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network. Results: Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC. Conclusion: These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time.