• 한국콘텐츠학회논문지
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• 제10권5호
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• pp.427-435
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• 2010

교과서에 쓰인 과학 용어 네트워크를 구축하여, 네트워크의 구조, 관련 정보 및 연관 관계를 분석하기 위하여 핵심용어를 도출하였다. 본 연구에서는 과학, 생물1 및 생물2 교과서 각 과목별로 출판사 세 곳을 선정하고 각각의 교과서에서 추출한 용어들을 노드로, 한 문장 안에 있는 과학 용어를 링크로 연결하여 네트워크를 구축하였다. 모든 교과서의 과학 용어 네트워크는 척도 없는(scale-free) 네트워크의 특성을 보여주었다. 복잡한 (complex) 네트워크에서 가중치가 낮은 것부터 제거하는 방법인 k-core 알고리즘을 적용하여 핵심 (core) 네트워크를 구축하였는데, 몇 개의 모듈이 연결되는 형태를 보여주었다. 과학교과서의 경우에는 물리, 화학, 생물, 지구과학 분야별로 크게 네 개의 모듈을 형성하였고, 생물1과 생물2 교과서는 각단원별로 용어들이 모여 있는 특성을 지닌 네트워크를 나타냈다. 본 연구에서 복잡한 네트워크에서 핵심네트워크를 구축하여 유용한 정보를 도출할 수 있는 가능성을 제시하였다.

• 대한예방한의학회지
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• 제21권2호
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• pp.1-13
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• 2017

Objectives : This paper serves to explore current trends of systems biology in Traditional Chinese Medicine (TCM) and examine how it may influence the Traditional Korean medicine. Methods : Literature review method was collectively used to classify Introduction to systems biology, diagnosis and syndrome classification of systems biology in TCM perspective, physiotherapy including acupuncture, herbs and formula functions, TCM systems biology, and directions of academic development. Results : The term 'Systems biology' is coined as a combination of systems science and biology. It is a field of study that tries to understand living organism by establishing a theory based on an ideal model that analyzes and predicts the desired output with understanding of interrelationships and dynamics between variables. Systems biology has an integrated and multi-dimensional nature that observes the interaction among the elements constructing the network. The current state of systems biology in TCM is categorized into 4 parts: diagnosis and syndrome, physical therapy, herbs and formulas and academic development of TCM systems biology and its technology. Diagnosis and syndrome field is focusing on developing TCM into personalized medicine by clarifying Kidney yin deficiency patterns and metabolic differences among five patterns of diabetes and analyzing plasma metabolism and biomarkers of coronary heart disease patients. In the field of physical therapy such as acupuncture and moxibustion, researchers discovered the effect of stimulating acupoint ST40 on gene expression and the effects of acupuncture on treating functional dyspepsia and acute ischemic stroke. Herbs and formulas were analyzed with TCM network pharmacology. The therapeutic mechanisms of Si Wu Tang and its series formulas are explained by identifying potential active substances, targets and mechanism of action, including metabolic pathways of amino acid and fatty acid. For the academic development of TCM systems biology and its technology, it is necessary to integrate massive database, integrate pharmacokinetics and pharmacodynamics, as well as systems biology. It is also essential to establish a platform to maximize herbal treatment through accumulation of research data and diseases-specific, or drug-specific network combined with clinical experiences, and identify functions and roles of molecules in herbs and conduct animal-based studies within TCM frame. So far, few literature reviews exist for systems biology in traditional Korean medicine and they merely re-examine known efficacies of simple substances, herbs and formulas. For the future, it is necessary to identify specific mechanisms of working agents and targets to maximize the effects of traditional medicine modalities. Conclusions : Systems biology is widely accepted and studied in TCM and already advanced into a field known as 'TCM systems biology', which calls for the study of incorporating TCM and systems biology. It is time for traditional Korean medicine to acknowledge the importance of systems biology and present scientific basis of traditional medicine and establish the principles of diagnosis, prevention and treatment of diseases. By doing so, traditional Korean medicine would be innovated and further developed into a personalized medicine.

• Journal of Microbiology and Biotechnology
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• 제32권3호
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• The Korean Journal of Ecology
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• 제23권2호
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• pp.89-100
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• 2000

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

• Genomics & Informatics
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• 제21권3호
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• pp.38.1-38.8
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• 2023

Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DEmiRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co- expression network. Next, four modules were selected based on the Z_summary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.

• 한국동물학회:학술대회논문집
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• 한국동물학회 1996년도 한국생물과학협회 국제학술대회
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• pp.147.2-147
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• 1996

No Abstract, See Full Text

• 한국동물학회:학술대회논문집
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• 한국동물학회 1999년도 한국생물과학협회 학술발표대회
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• pp.37-37
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• 1999

No Abstract, See Full Text

• Genomics & Informatics
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• 제14권1호
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• pp.2-11
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• 2016

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

• 한국당과학회:학술대회논문집
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• 한국당과학회 2006년도 제1회 연례학술대회
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• pp.48-48
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• 2006

• 한국동물학회:학술대회논문집
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• 한국동물학회 2000년도 한국생물과학협회 학술발표대회
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• pp.90.2-91
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• 2000

No Abstract, See Full Text