• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.39-48
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• 2022

Today the regulation of socio-economic development has been the subject of active scientific debate. The modern paradigm of regulatory policy in foreign countries involves a change in the role and strategy of the state, which determines the relevance of this topic. The aim of the article is to study the current state of regulatory policy research. The article is based on a bibliographic analysis of the study of regulatory policy. The study is based on the data search functions of the Scopus platform. It uses a set of VOSviewer program, online visualization of keywords in the titles of scientific journals and citations of publications. The study led to the conclusion that the number of publications that directly study the nature and features of regulatory policy is insignificant, but constantly growing. In our opinion, further research should determine the essence of regulatory policy as a separate category, a description of its features and factors of formation. It is also necessary to develop a common concept that governments should be actively involved in ensuring the quality of regulation, rather than responding to the shortcomings of regulation, which is evolving into regulatory governance.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.9
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• pp.3677-3683
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• 2015

Abnormally expressed microRNAs (miRNAs) and genes have been found to play key roles in esophageal squamous cell carcinoma (ESCC), but little is known about the underlying mechanisms. The aim of this paper was to assess inter-relationships and the regulatory mechanisms of ESCC through a network-based approach. We built three regulatory networks: an abnormally expressed network, a related network and a global network. Unlike previous examples, containing information only on genes or miRNAs, the prime focus was on relationships. It is worth noting that abnormally expressed network emerged as a fault map of ESCC. Theoretically, ESCC might be treated and prevented by correcting the included errors. In addition, the predicted transcription factors (TFs) obtained by the P-match method also warrant further study. Our results may further guide gene therapy researchers in the study of ESCC.

• IMMUNE NETWORK
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• v.7 no.4
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• pp.167-172
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• 2007

Various cell types that express regulatory function may influence the pathogenesis of most and perhaps all infections. Some regulatory cells are present at the time of infection whereas others are induced or activated in response to infection. The actual mechanisms by which different types of infections signal regulatory cell responses remain poorly understood. However a most likely mechanism is the creation of a microenvironment that permits the conversion of conventional T cells into cells with the same antigen specificity that have regulatory function. Some possible means by which this can occur are discussed. The relationship between regulatory cells and infections is complex especially with chronic situations. The outcome can either be of benefit to the host or damage the disease control process or in rare instances appears to be a component of a finely balanced relationship between the host and the infecting agent. Manipulating the regulatory cell responses to achieve a favorable outcome of infection remains an unfulfilled objective of therapeutic immunology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Journal of Microbiology and Biotechnology
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• v.16 no.6
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• pp.993-998
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• 2006

We earlier suggested a hierarchical regulatory network using defined modeling symbols and weights in order to improve the flux balance analysis (FBA) with regulatory events that were represented by if-then rules and Boolean logic. In the present study, the simulation results of the models, which were developed and improved from the previou model by incorporating a hierarchical regulatory network into the FBA, were compared with the experimental outcome of an aerobic batch growth of E. coli on glucose and tryptophan. From the experimental result, a diauxic growth curve was observed, reflecting growth resumption, when tryptophan was used as an alternativee after the supply of glucose was exhausted. The model parameters, the initial concentration of substrates (0.92 mM glucose and 1 mM tryptophan), cell density (0.0086 g biomass/1), the maximal uptake rates of substrates (5.4 mmol glucose/g DCW h and 1.32 mmol tryptophan/g DCW h), and lag time (0.32 h) were derived from the experimental data for more accurate prediction. The simulation results agreed with the experimental outcome of the temporal profiles of cell density and glucose, and tryptophan concentrations.

• Genomics & Informatics
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• v.11 no.4
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.237-240
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• 2007

본 논문에서는 유전자 발현데이터로부터 유전자 조절네트워크를 추론하는 유전자 알고리즘을 제안한다. 근래에 유전자 알고리즘을 이용하여 유전자 조절네트워크를 추론하려는 시도가 있었으나 그리 성공적이지 못하였다. 우리는 본 논문에서 유전자 조절네트워크를 보다 효율적으로 추론할 수 있게 하기 위하여 새로운 유전자 인코딩 기법을 개발하여 적용하였다. 선형 유전자 조절네트워크로 모델링 된 인공 유전자 조절네트워크를 사용하여 실험한 결과 대부분의 경우에 있어서 주어진 인공 유전자 조절네트워크와 유사한 네트워크를 추론하였으며 완전히 동일한 유전자네트워크를 추론하기도 하였다. 향후 실제 유전자 발현 데이터를 이용하여 추론해 보는 것이 필요하다.

• IMMUNE NETWORK
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• v.14 no.5
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• pp.227-236
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• 2014

Understanding germinal center reactions is crucial not only for the design of effective vaccines against infectious agents and malignant cells but also for the development of therapeutic intervention for the treatment of antibody-mediated immune disorders. Recent advances in this field have revealed specialized subsets of T cells necessary for the control of B cell responses in the follicle. These cells include follicular regulatory T cells and Qa-1-restricted cluster of differentiation $(CD)8^+$ regulatory T cells. In this review, we discuss the current knowledge related to the role of regulatory T cells in the B cell follicle.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2265-2268
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• 2013

Several lines of evidence indicate that cancer is a multistep process. To survey the mechanisms involving gene alteration and miRNAs in adrenocortical cancer, we focused on transcriptional factors as a point of penetration to build a regulatory network. We derived three level networks: differentially expressed; related; and global. A topology network ws then set up for development of adrenocortical cancer. In this network, we found that some pathways with differentially expressed elements (genetic and miRNA) showed some self-adaption relations, such as EGFR. The differentially expressed elements partially uncovered mechanistic changes for adrenocortical cancer which should guide medical researchers to further achieve pertinent research.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.