• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.1-13
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• 2007

In this research, we found out that bridging nodes have great effect on the robustness of protein-protein interaction networks. Until now, many researchers have focused on node's degree as node's essentiality. Hub nodes in the scale-free network are very essential in the network robustness. Some researchers have tried to relate node's essentiality with node's betweenness centrality. These approaches with betweenness centrality are reasonable but there is a positive relation between node's degree and betweenness centrality value. So, there are no differences between two approaches. We first define a bridging node as the node with low connectivity and high betweenness value, we then verify that such a bridging node is a primary factor in the network robustness. For a biological network database from Internet, we demonstrate that the removal of bridging nodes defragment an entire network severally and the importance of the bridging nodes in the network robustness.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.55-56
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• 2014

• 한국당과학회:학술대회논문집
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• 2006.11a
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• pp.48-48
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• 2006

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.474-492
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• 2007

In the protein interaction network, there are many meaningful functional modules, each involving several protein interactions to perform discrete functions. Pathways and protein complexes are the examples of the functional modules. In this paper, we propose a new method for detecting the functional modules based on concept. A conceptual functional module, briefly concept module is introduced to match the modules taking them as its instances. It is defined by the corresponding rule composed of triples and operators between the triples. The triples represent conceptual relations reifying the protein interactions of a module, and the operators specify the structure of the module with the relations. Furthermore, users can define a composite concept module by the counterpart rule which, in turn, is defined in terms of the predefined rules. The concept module makes it possible to detect functional modules that are conceptually similar as well as structurally identical to users' queries. The rules are managed in the XML format so that they can be easily applied to other networks of different species. In this paper, we also provide a visualized environment for intuitionally describing complexly structured rules.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Proceedings of the Microbiological Society of Korea Conference
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• 2004.05a
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• pp.65-67
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• 2004

O-GlcNAcylation of p53 has been already identified and reported, but the function of O-GlcNAc on p53 has not been studied well. In this report, the general function of O-GlcNAc modification on p53 has been investigated using mouse fibroblast cell, L929. When streptozotocin (STZ), a non-competitive O-GlcNAcase inhibitor was treated to L929, O-GlcNAc modification level was dramatically increased on nucleocytoplasmic proteins, including p53. Because it has been already reported that $TNF\alpha$ induced the production of p53 in L929, $TNF\alpha$ was treated to obtain more p53. Approximately two times more amount of p53 was found from the cells treated STZ and $TNF\alpha$ simultaneously compared to the cell treated $TNF\alpha$ alone. The p53 increment in the presence of STZ was not caused by the induction of p53 gene expression. When new production of p53 induced by the $TNF\alpha$ was inhibited by the treatment of cycloheximide, O-GlcNAc modification decreased and phosphorylation increased on pre-existing p53 after $TNF\alpha$ treatment. But in the presence of STZ and $TNF\alpha$ at the same time, more O-GlcNAcylation occurred on p53, The level of ubiquitination on p53 was also reduced in the presence of STZ. Approximately three times less amount of Mdm2 bound to this hyperglycosylated p53. From this result it might be concluded that treatment of STZ to inhibit O-GlcNAcase increased O-GlcNAc modification level on p53 and the increment of O-GlcNAc modification stabilized p53 from ubiquitin proteolysis system.

• Journal of TMJ Balancing Medicine
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• v.10 no.1
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• pp.12-20
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• 2020

Objectives: Tourette syndrome (TS) is a disease that occurs evenly in many social classes. Despite the long experience of drug treatment, the preference is low due to various side effects. The aim of this study was to discover herbal medicine resources through network pharmacology analysis predicted to be useful for Tourette syndrome. Methods: We used Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) to identify herbal medicines that can be used for TS by using network pharmacology research methods and to predict the mechanism of action. After evaluating compounds of each identified herb, molecular target proteins and mechanisms of action were analyzed, focusing on compounds that are likely to exhibit clinical activity in consideration of the pharmacokinetic parameters of these individual compounds. Results: Fifty nine ingredients such as atropine, veraguensin, and nuciferin among the compounds contained in 48 types of medicinal herbs such as Daturae Flos (洋金花), Salviae Radix (丹参), and Nelumbinis Plumula (蓮子心) act on the D(2) dopamine receptor, which is a protein involved in the development of TS. It has been found that atropine, veraguensin, and nuciferin are highly likely to exhibit activity by acting on the G protein-coupled receptor signaling pathway. Conclusions: It can be used in conjunction with non-invasive treatment means such as FCST Yinyang Balancing Appliance with herbal therapy to bring about a significant therapeutic effect, and it will be possible to develop a treatment that can replace drug therapy used in Western medicine.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.697-709
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• 2022

Objective : The present study aimed to identify the function of ischemic stroke (IS) patients' peripheral blood and its role in IS, explore the pathogenesis, and provide direction for clinical research progress by comprehensive bioinformatics analysis. Methods : Two datasets, including GSE58294 and GSE22255, were downloaded from Gene Expression Omnibus database. GEO2R was utilized to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the database annotation, visualization and integrated discovery database. The protein-protein interaction (PPI) network of DEGs was constructed by search tool of searching interactive gene and visualized by Cytoscape software, and then the Hub gene was identified by degree analysis. The microRNA (miRNA) and miRNA target genes closely related to the onset of stroke were obtained through the miRNA gene regulatory network. Results : In total, 36 DEGs, containing 27 up-regulated and nine down-regulated DEGs, were identified. GO functional analysis showed that these DEGs were involved in regulation of apoptotic process, cytoplasm, protein binding and other biological processes. KEGG enrichment analysis showed that these DEGs mediated signaling pathways, including human T-cell lymphotropic virus (HTLV)-I infection and microRNAs in cancer. The results of PPI network and cytohubba showed that there was a relationship between DEGs, and five hub genes related to stroke were obtained : SOCS3, KRAS, PTGS2, EGR1, and DUSP1. Combined with the visualization of DEG-miRNAs, hsa-mir-16-5p, hsa-mir-181a-5p and hsa-mir-124-3p were predicted to be the key miRNAs in stroke, and three miRNAs were related to hub gene. Conclusion : Thirty-six DEGs, five Hub genes, and three miRNA were obtained from bioinformatics analysis of IS microarray data, which might provide potential targets for diagnosis and treatment of IS.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.6
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• pp.3669-3676
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• 2013

Faithful transmission of genetic information depends on accurate chromosome segregation as cells exit from mitosis, and errors in chromosomal segregation are catastrophic and may lead to aneuploidy which is the hallmark of cancer. In eukaryotes, an elaborate molecular control system ensures proper orchestration of events at mitotic exit. Phosphorylation of specific tyrosyl residues is a major control mechanism for cellular proliferation and the activities of protein tyrosine kinases and phosphatases must be integrated. Although mitotic kinases are well characterized, phosphatases involved in mitosis remain largely elusive. Here we identify a novel variant of mouse protein tyrosine phosphatase containing domain 1 (Ptpcd1), that we named Ptpcd2. Ptpcd1 is a Cdc14 related centrosomal phosphatase. Our newly identified Ptpcd2 shared a significant homology to yeast Cdc14p (34.1%) and other Cdc14 family of phosphatases. By subcellular fractionation Ptpcd2 was found to be enriched in the cytoplasm and nuclear pellets with catalytic phosphatase activity. By means of immunofluorescence, Ptpcd2 was spatiotemporally regulated in a cell cycle dependent manner with cytoplasmic abundance during mitosis, followed by nuclear localization during interphase. Overexpression of Ptpcd2 induced mitotic exit with decreased levels of some mitotic markers. Moreover, Ptpcd2 failed to colocalize with the centrosomal marker ${\gamma}$-tubulin, suggesting it as a non-centrosomal protein. Taken together, Ptpcd2 phosphatase appears a non-centrosomal variant of Ptpcd1 with probable mitotic functions. The identification of this new phosphatase suggests the existence of an interacting phosphatase network that controls mammalian mitosis and provides new drug targets for anticancer modalities.

• Journal of Microbiology and Biotechnology
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• v.22 no.8
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• pp.1054-1058
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• 2012

In order to predict biologically significant attributes such as function from protein sequences, searching against large databases for homologous proteins is a common practice. In particular, BLAST and HMMER are widely used in a variety of biological fields. However, sequence-homologous proteins determined by BLAST and proteins having the same domains predicted by HMMER are not always functionally equivalent, even though their sequences are aligning with high similarity. Thus, accurate assignment of functionally equivalent proteins from aligned sequences remains a challenge in bioinformatics. We have developed the FEP-BH algorithm to predict functionally equivalent proteins from protein-protein pairs identified by BLAST and from protein-domain pairs predicted by HMMER. When examined against domain classes of the Pfam-A seed database, FEP-BH showed 71.53% accuracy, whereas BLAST and HMMER were 57.72% and 36.62%, respectively. We expect that the FEP-BH algorithm will be effective in predicting functionally equivalent proteins from BLAST and HMMER outputs and will also suit biologists who want to search out functionally equivalent proteins from among sequence-homologous proteins.