- P-ISSN 1010-0695
- E-ISSN 2288-3339
- Apply for Authority
- P-ISSN1010-0695
- E-ISSN2288-3339
Article Detail
Home > Article Detail
A Structural Analysis of Sanghanron by Network Model -Centered on Symptoms and Herbs of Taeyangbyung Compilation in Sanghanron-
Journal of Korean Medicine / Journal of Korean Medicine, (P)1010-0695; (E)2288-3339
2011, v.32 no.1, pp.56-66
&
(2011). A Structural Analysis of Sanghanron by Network Model -Centered on Symptoms and Herbs of Taeyangbyung Compilation in Sanghanron-. Journal of Korean Medicine, 32(1), 56-66.
- Downloaded
- Viewed
Abstract
Background: This was a study to analyze Sanghanron through network theory, as the first attempt to construct network models for systems biomedicine in traditional Korean medicine. For this purpose, we investigated the network structure with priority given to two-node connections between symptoms and herbs of Taeyangbyung compilation in Sanghanron. Purpose: We had three goals in carrying out this study. First, to establish the minimum clinical grouping data sets for symptoms and herbs of Taeyangbyung compilation in Sanghanron. Second, to make index files for the obtained data sets. Third, to generate a network structure for systems biomedicine in this part, and analyze its relationship. Methods: Using MS office Excel and Netminer software, we constructed the minimum clinical grouping data sets and the network for systems biomedicine about symptoms and herbs of Taeyangbyung compilation in Sanghanron, and analyzed its relationship. Results: We established the minimum clinical grouping data sets for symptoms and herbs of Taeyangbyung compilation in Sanghanron, using MS Excel. We constructed a network to structurize our database through two-node connections of Netminer program, and analyzed its relationships. Conclusions: Further research on network model for systems biomedicine between symptoms and herbs for three Yang and three Um(Taeyang, Soyang, Yangmyung, Taeum, Soum, Gualum) disease compilation is necessary.
- keywords
- network, symptoms, herbs, Sanghanron, node
Reference
1
1. Park HA, Kim JU, Cho IS. Nursing diagnosis, intervention, and outcome classification. Seoul University. 1999.
2
2. Mun JJ, An KS, Kim SH, Um HS, Ji KY, Km JB. Detailed interpretation about Shanghanron. 1996.
3
3. Getting Started with Netminer. Cyram
4
4. Nah DB. An information analysis study on technology transfer. Graduate school of digital small business, Hansung University. 2006.
5
5. Won JK. A framework : Organizational network discovery on workflows, Graduate school, Kyonggi University. 2008.
6
6. Lee SH. The social network analysis on the interpersonal relationship of the elementary school Taekwondo training students. Graduate school of Inha University. 2007.
7
7. Choi HS. Korean professional football players among the human network analysis. Graduate school of education, Yong-in University. 2008.
8
8. Nam SH. A study on the visualization of human network through tag. Graduate school of education, Hong-ik University. 2009
9
9. Jin HJ, Kim MG, Kim JY. Development of Decision tree program based on Web for analyzing clinical information of Sasang constitutional medicine. Korean journal of Oriental medicine. 2008;14(3):81-87.
10
10. Jin HJ, Moon JS, Go SH, Ku IH, Lee SW, Lee DH, et. al. Preliminary study to establish a decision support system in Sasang constitutional medicine with clinical data. Korean journal of Oriental medicine. 2007;13(2):75-105.
11
11. Kim SK, Kim C, Jin HJ, Song MY. Construction of input system for effective collection of Constitution clinical information. Korean journal of Oriental medicine. 2008;14(1):97-105.
12
12. Sim IC, Oh SK, Kim HK. Standard development of Korea knowledge portal ontology. Korea agency for digital opportunity and promotion.
13
13. Park KM, Lim HS, Park JH. Building an ontology for structured data entry of signs and aymptoms in Oriental medicine. Korean journal of Oriental physiology and pathology. 2003; 17(5):1151-1156.
14
14. Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, et.al. Nature, “Comparative assessment of large-scale data sets of proteinprotein interactions”. 2002.
15
15. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL. PNAS. The human disease network. Department of Genetics, Harvard Medical School. 2007;104(21). 8685-8690.
16
16. Emig D, Salomonis N, Baumbach J, Lengauer T, Conklin BR, Albrecht M. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Research, 2010.
17
17. Moore JH, Asselbergs FW, Williams SM. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010.
18
18. Lopes CT, Franz M, Kazi F, Donaldson SL, Gary QM. Cytoscape Web: an interactive web-based network browser. Bioinformatics. 2010.
19
19. Ishii N, Koike A, Yamamoto Y, Takagi T. Figure classification in biomedical literature to elucidate disease mechanisms, based on pathways. Artificial Intelligence in Medicine. 2010.
20
20. Laukens K, Hollunder J, Thang HD, Jaeger DG, Kuiper M, Witters E, et, al. Flexible network reconstruction from relational databases with Cytoscape and CytoSQL. BMC Bioinformatics. 2010.
21
21. Lee GW, Kim SS. Genome data mining for everyone. BMB reports.
22
22. Shiraishi T, Matsuyama S, Kitano H. Large-Scale Analysis of Network Bistability for Human Cancers. PLoS Computational Biology. 2010.
23
23. Pedamallu CS, Posfai J. Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information. Source Code for Biology and Medicine. 2010.
24
24. Huang T, Wang P, Ye ZQ, Xu H, He ZS, Feng KY, et, al. Prediction of Deleterious Non- Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties. PLoS ONE. 2010.
25
25. Nandy SK, Jouhten P, Nielsen J. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation. BMC Systems Biology 2010.
26
26. Bittner L, Halary S, Payri C, Cruaud C, Reviers B, Lopez P, et, al. Some considerations for analyzing biodiversity using integrative metagenomics and gene networks. Biology Direct. 2010.
27
27. Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009.
28
28. Martin A, Ochagavia ME, Rabasa LC, Miranda J, Cossio JF, Bringas R. BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinformatics. 2010.
29
29. Kaimal V, Bardes EE, Tabar SC, Jegga AG, Aronow BJ. ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and networkbased dissection of biological systems. Nucleic Acids Research. 2010.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations
Other articles from this issue
- Study on Pulse Simulator of Oriental Medicine for Objective Structured Clinical Examination (OSCE)
- Study of Toxicity Presence Classification about Herbal Diet in Tang-aec-pyeon of Dong-ui-bo-gam
- A Comparison Study of Factors of Willingness to Use Traditional Medicine between Korean, Chinese and Taiwanese
- A Structural Analysis of Sanghanron by Network Model -Centered on Symptoms and Herbs of Taeyangbyung Compilation in Sanghanron-
- Single Dose Oral Toxicity and Genotoxicological Safety Study of Ssanghwa-tang Fermented with Lactobacillus acidophyllus
- Some Guidelines for Academic Sample Survey Studies in Korea Traditional Medicine
- Determinants of the Utilization of Oriental Medical Services by the Elderly
- Effects of KHchunggan-tang on the Nonalcoholic Fatty Liver Disease in Palmitate-induced Cellular Model
- Developing a Health Informatics Conceptual Framework for Representing Clinical Findings in Traditional East Asian Medicine
- Effects of Chunghyuljihwang-tang on Phenylhydrazine-induced Hemolytic Anemia in Rats
- Evaluation of Antioxidant Activity of Solvent Fractions of Angelica gigas Root Using TOSC Assay
- The Effects of Persicae Semen Herbal-acupuncture at GB34 (Yangneungcheon) on a Rat Model of Hyperlipidemia
- Effects of Concurrent Administration of Hyeolbuchukeo-tang and Aspirin on Atherosclerosis in the ApoE^((-/-)) Mouse
- Effects of Angelicae Pubescentis Radix Water Extract on Immune Property in RAW 264.7 Macrophages
- more
Recommanded Articles
This website recommended the use the Chrome browser for journal website.