• Genomics & Informatics
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• v.18 no.1
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• pp.11.1-11.3
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• 2020

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

• Genomics & Informatics
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• v.16 no.4
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• pp.39.1-39.3
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• 2018

The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1155-1163
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• 2004

Extracellular signal-regulated kinase (ERK) signaling pathway is one of the mitogen-activated protein kinase (MAPK) signal transduction pathways. This pathway is known as pivotal in many signaling networks that govern proliferation, differentiation and cell survival. The ERK signaling pathway comprises positive and negative feedback loops, depending on whether the terminal kinase stimulates or inhibits the activation of the initial level. In this paper, we attempt to model the ERK pathway by considering both of the positive and negative feedback mechanisms based on Michaelis-Menten kinetics. In addition, we propose a fraction ratio model based on the mass action law. We first develop a mathematical model of the ERK pathway with fraction ratios. Secondly, we analyze the dynamical properties of the fraction ratio model based on simulation studies. Furthermore, we propose a concept of an inhibitor, catalyst, and substrate (ICS) controller which regulates the inhibitor, catalyst, and substrate concentrations of the ERK signal transduction pathway. The ICS controller can be designed through dynamical analysis of the ERK signaling transduction pathway within limited concentration ranges.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.467-475
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• 2014

With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. A pathway is the biological deep knowledge that represents the relations of dynamics and interaction among proteins, genes and cells by a network. A pathway is wildly being used as an important part of a bio-medical big-data analysis. However, a pathway analysis requires a lot of time and effort because a pathway is very diverse and high volume. Also, multidimensional analysis systems for various pathways are nonexistent even now. In this paper, we proposed a pathway analysis system that collects user interest pathways from KEGG pathway database that supports the most widely used pathways, constructs a network based on a hierarchy structure of pathways and analyzes the relations of dynamics and interaction among pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our pathway analysis system, we evaluate the performance of our system in various experiments.

• The Korean Journal of Physiology and Pharmacology
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• v.20 no.4
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• pp.367-378
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• 2016

Recently, it was reported that the role of mitochondria-reactive oxygen species (ROS) generating pathway in cisplatin-induced apoptosis is remarkable. Since a variety of molecules are involved in the pathway, a comprehensive approach to delineate the biological interactions of the molecules is required. However, quantitative modeling of the mitochondria-ROS generating pathway based on experiment and systemic analysis using the model have not been attempted so far. Thus, we conducted experiments to measure the concentration changes of critical molecules associated with mitochondrial apoptosis in both human mesothelioma H2052 and their ${\rho}^0$ cells lacking mitochondrial DNA (mtDNA). Based on the experiments, a novel mathematical model that can represent the essential dynamics of the mitochondrial apoptotic pathway induced by cisplatin was developed. The kinetic parameter values of the mathematical model were estimated from the experimental data. Then, we have investigated the dynamical properties of this model and predicted the apoptosis levels for various concentrations of cisplatin beyond the range of experiments. From parametric perturbation analysis, we further found that apoptosis will reach its saturation level beyond a certain critical cisplatin concentration.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3905-3909
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• 2012

Ovarian cancer is the fifth leading cause of cancer death in women aged 35 to 74 years. Although there are several popular hypothesis of ovarian cancer pathogenesis, the genetic mechanisms are far from being clear. Recently, systems biology approaches such as network-based methods have been successfully applied to elucidate the mechanisms of diseases. In this study, we constructed a crosstalk network among ovarian cancer related pathways by integrating protein-protein interactions and KEGG pathway information. Several significant pathways were identified to crosstalk with each other in ovarian cancer, such as the chemokine, Notch, Wnt and NOD-like receptor signaling pathways. Results from these studies will provide the groundwork for a combination therapy approach targeting multiple pathways which will likely be more effective than targeting one pathway alone.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.705-713
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• 2010

Predicting survival from microarray data is not easy due to the problem of high dimensionality of data and the existence of censored observations. Also the limitation of individual gene analysis causes the shift of focus to the level of gene sets with functionally related genes. For developing a survival prediction model based on pathway information, the methods for selecting a supergene using principal component analysis and testing its significance for each pathway are discussed. Besides, the performance of gene filtering is compared.

• Genomics & Informatics
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• v.6 no.4
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• pp.223-226
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• 2008

We introduce a computational approach for analysis of glycosylation in Post-PKS tailoring steps. It is a computational method to predict the deoxysugar biosynthesis unit pathway and the substrate specificity of glycosyltransferases involved in the glycosylation of polyketides. In this work, a directed and weighted graph is introduced to represent and predict the deoxysugar biosynthesis unit pathway. In addition, a homology based gene clustering method is used to predict the substrate specificity of glycosyltransferases. It is useful for the rational design of polyketide natural products, which leads to in silico drug discovery.

• 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 Academy of Nursing Administration
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• v.16 no.1
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• pp.59-72
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• 2010

Purpose: This study is a descriptive research to analyze prognostic factors of barbiturate coma therapy (BCT) for severe brain damage patients, to develop a critical pathway (CP) based on the results of analysis and to examine the effect of its clinical application. Method: We analyzed medical records of 76 patients who received BCT for more than three days between January 1999 to July 2005. Based on the results of the analysis, we developed a CP and applied it to 12 people during August-December of 2005. Result: By application of BCT CP, the mortality rate decreased from 31.6% to 16.7%. It was found that the period of staying at ICU and total period of hospitalization were shortened by 2.78 (13.9%) days and 16.43 (29.4%) days, respectively. The Glasgow coma scale of the recovery group by CP application was 9.03 (4.64) at 72 hours post of BCT and 14.28 (1.82) at discharge from hospital, and DRS was 6.62 (6.38) points. Conclusion: By verifying clinical validity of the suggested CP, we believe that we have obtained visible effects standardizing the treatment pathway of BCT for brain damage patients.