• Journal of the Korea Knowledge Information Technology Society
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• v.13 no.5
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• pp.607-614
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• 2018

Oryeong-san is a prescription widely used for diseases where water is stagnant because it has the effect of circulating the water in the body and releasing it into the urine. In order to investigate the mechanisms of oryeong-san, we in this paper construct and analysis the compound-target network of medicinal materials constituting oryeong-san based on a systems pharmacology approach. First, the targets related to the 475 chemical compounds of oryeong-san were searched in the STITCH database, and the search results for the interactions between compounds and targets were downloaded as XML files. The compound-target network of oryeong-san is visualized and explored using Gephi 0.8.2, which is an open-source software for graphs and networks. In the network, nodes are compounds and targets, and edges are interactions between the nodes. The edge is weighted according to the reliability of the interaction. In order to analysis the compound-target network, it is clustered using MCL algorithm, which is able to cluster the weighted network. A total of 130 clusters were created, and the number of nodes in the cluster with the largest number of nodes was 32. In the clustered network, it was revealed that the active compounds of medicinal materials were associated with the targets for regulating the blood pressure in the kidney. In the future, we will clarify the mechanisms of oryeong-san by linking the information on disease databases and the network of this research.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.213-219
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• 2018

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim's positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.

• BMB Reports
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• v.55 no.9
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• pp.417-428
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• 2022

Herbal medicine, a multi-component treatment, has been extensively practiced for treating various symptoms and diseases. However, its molecular mechanism of action on the human body is unknown, which impedes the development and application of herbal medicine. To address this, recent studies are increasingly adopting systems pharmacology, which interprets pharmacological effects of drugs from consequences of the interaction networks that drugs might have. Most conventional network-based approaches collect associations of herb-compound, compound-target, and target-disease from individual databases, respectively, and construct an integrated network of herb-compound-target-disease to study the complex mechanisms underlying herbal treatment. More recently, rapid advances in high-throughput omics technology have led numerous studies to exploring gene expression profiles induced by herbal treatments to elicit information on direct associations between herbs and genes at the genome-wide scale. In this review, we summarize key databases and computational methods utilized in systems pharmacology for studying herbal medicine. We also highlight recent studies that identify modes of action or novel indications of herbal medicine by harnessing drug-induced transcriptome data.

• The Journal of Korean Medicine
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• v.45 no.1
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• pp.114-125
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• 2024

Objectives: Network pharmacology is an analysis method that explores drug-centered efficacy and mechanism by constructing a compound-target-disease network based on system biology, and is attracting attention as a methodology for studying herbal medicine that has the characteristics for multi-compound therapeutics. Thus, we investigated the potential functions and pathways of Myrrha on Allergic Rhinitis (AR) via network pharmacology analysis and molecular docking. Methods: Using public databases and PubChem database, compounds of Myrrha and their target genes were collected. The putative target genes of Myrrha and known target genes of AR were compared and found the correlation. Then, the network was constructed using STRING database, and functional enrichment analysis was conducted based on the Gene Ontology (GO) Biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways. Binding-Docking stimulation was performed using CB-Dock. Results: The result showed that total 3 compounds and 55 related genes were gathered from Myrrha. 33 genes were interacted with AR gene set, suggesting that the effects of Myrrha are closely related to AR. Target genes of Myrrha are considerably associated with various pathways including 'Fc epsilon RI signaling pathway' and 'JAK-STAT signaling pathway'. As a result of blinding docking, AKT1, which is involved in both mechanisms, had high binding energies for abietic acid and dehydroabietic acid, which are components of Myrrha. Conclusion: Through a network pharmacological method, Myrrha was predicted to have high relevance with AR by regulating AKT1. This study could be used as a basis for studying therapeutic effects of Myrrha on AR.

• Herbal Formula Science
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• v.28 no.3
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• pp.243-254
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• 2020

Objectives : Network pharmacology analysis is commonly used to investigate the synergies and potential mechanisms of multiple compounds by analyzing complex, multi-layered networks. We used TCMSP and BATMAN-TCM databases to compare results of network pharmacological analysis between White Ginseng(WG) and Red Ginseng(RG). Methods : WG and RG were compared with components and their target molecules using TCMSP database, and compound-target-pathway/disease networks were compared using BATMAN-TCM database. Results : Through TCMSP, 104 kinds of target molecules were derived from WG and 38 kinds were derived from RG. Using the BATMAN-TCM database, target pathways and diseases were screened, and more target pathways and diseases were screened compared to RG due to the high composition of WG ingredients. Analysis of component-target-pathway/disease network using network analysis tools provided by BATMAN-TCM showed that WG formed more networks than RG. Conclusions : Network pharmacology analysis can be effectively performed using various databases used in system biology research, and although the materials that have been reported in the past can be used efficiently for research on diseases related to targets, the results are unreliable if prior studies are focused on limited or narrow research areas.

• Genomics & Informatics
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• v.9 no.4
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• pp.173-180
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• 2011

Recent trends in generating multiple, large-scale datasets provide new challenges to manipulating the relationship of different types of components, such as gene expression and drug response data. Integrative analysis of compound response and gene expression datasets generates an opportunity to capture the possible mechanism of compounds by using signature genes on diverse types of cancer cell lines. Here, we integrated datasets of compound response and gene expression profiles on NCI60 cell lines and constructed a network, revealing the relationship for 801 compounds and 341 gene probes. As examples, obtusol, which shows an exclusive sensitivity on a small number of colon cell lines, is related to a set of gene probes that have unique overexpression in colon cell lines. We also found that the SLC7A11 gene, a direct target of miR-26b, might be a key element in understanding the action of many diverse classes of anticancer compounds. We demonstrated that this network might be useful for studying the mechanisms of varied compound response on diverse cancer cell lines.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.113-121
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• 2023

Network pharmacology in traditional Korean and Chinese medicine studies the molecular and biological aspects of herbal medicine using computational methods. Despite variations in databases, techniques, and criteria, most studies follow similar steps: constructing herb-compound networks, compound-target networks, and target interpretation. To ensure efficient and consistent analysis in herbal medicine network pharmacology, we designed and implemented a common analysis pipeline. We showed its reliability with existing databases. The proposed system has a potential to facilitate network pharmacology analysis in traditional medicine, ensuring consistent analysis of various herbal medicines.

• Herbal Formula Science
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• v.27 no.3
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• pp.189-197
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• 2019

Objectives : The purpose of this study was to confirm whether Codonopsis Radix(CR) could be used in the same way for expected indications or diseases of adaptation instead of Ginseng Radix(GR), which acts as a principal herb in Sagunja-tang. Methods : The Traditional Chinese Medicine Systems pharmacology(TCMSP), a database for the study of systems biology related to Chinese medicine, screened potential active compounds in each quartet. By searching for all the proteins that each compound provides, the target of Sagunja-tang with GR(GRST) and the target of Sagunja-tang with CR(CRST) were compared using the network analysis method, and the top ranked target of each serving was selected. Results : Through TCMSP, a Chinese medicine database, the potential effective ingredients of GRST or CRST screened, and the target proteins related to these substances were found to be the most affected by Glycyrrhizae Radix et Rhizome, an herbal medicine mixed in Sagunja-tang, and the target diseases were the same. And the same were found for the target protein, gene and target diseases of GRST and CRST. Conclusions : The prescription with similar composition is likely to have similar network pharmacology analysis results, and the analysis result may be controlled by the herbal medicines which are assumed to be the main function. Therefore, rich and reproducible basic studies is more important because network pharmacological studies can be dominated by data that has been done a lot of previous studies.

• Herbal Formula Science
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• v.31 no.4
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• pp.245-252
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• 2023

Objectives : The purpose of this study was to explore the compounds, targets and related diseases of garlic by the approaches of network pharmacology and bioinformatics in traditional chinese medicine. Methods : We investigated components and their target molecules of garlic using SymMap and TCMSP and they were compared with analysis platform. Results : 56 potential compounds were identified in garlic, 26 of which contained target information, and it was found that these 26 compounds and 154 targets interact with each other through a combination of 243 compounds. In addition, Apigenin was linked to the most targeted gene (78) in 26 compounds, followed by Kaempferol (61 genes), Nicotic Acid (14 genes), Geraniol (11 genes), Eee (10 genes), and Sobrol A (9 genes). Among 56 potential compounds, three compounds (Kaempferol, Dipterocarpol, and N-Methyl cytisine) corresponded to the active compound by screening criterion Absorption, Distribution, Metabolism, Excretion (ADME). In addition, 12 compounds in 56 potential compounds were associated with gastrointestinal (GI) motility disorder. Among them, Kaempferol was a compound that met the ADME parameters and the rest were potential compounds that did not meet. Also, Kaempferol was closely related to GI motility disorder, indicating that this Kaempferol could be a candidate for potential medical efficacy. Conclusions : It shows the relationship between the compound of garlic, an herbal supplement, and the biological process associated with GI motility disorder. These results are thought to help develop strategies for treating GI motility disorders.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.