• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.202-205
• /
• 2005

Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.339-343
• /
• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.11
• /
• pp.6631-6636
• /
• 2013

Through years of effort, researchers have made notable progress in gene and microRNA fields about retinoblastoma morbidity. However, experimentally validated data for genes, microRNAs (miRNAs) and transcription factors (TFs) can only be found in a scattered form, which makes it difficult to conclude the relationship between genes and retinoblastoma systematically. In this study, we regarded genes, miRNAs and TFs as elements in the regulatory network and focused on the relationship between pairs of examples. In this way, we paid attention to all the elements macroscopically, instead of only researching one or several. To show regulatory relationships over genes, miRNAs and TFs clearly, we constructed 3 regulatory networks hierarchically, including a differentially expressed network, a related network and a global network, for analysis of similarities and comparison of differences. After construction of the three networks, important pathways were highlighted. We constructed an upstream and downstream element table of differentially expressed genes and miRNAs, in which we found self-adaption relations and circle-regulation. Our study systematically assessed factors in the pathogenesis of retinoblastoma and provided theoretical foundations for gene therapy researchers. In future studies, especial attention should be paid to the highlighted genes and miRNAs.

• IMMUNE NETWORK
• /
• v.10 no.6
• /
• pp.247-249
• /
• 2010

Foxp3 is a transcript factor for regulatory T cell development. Interestingly, Foxp3-expressing cells were identified in B cells, especially in CD19(+)CD5(+) B cells, while those were not examined in CD19(+)CD5(-) B cells. Foxp3-expressing CD5(+) B cells in this study were identified in human PBMCs and were found to consist of $8.5{\pm}3.5%$ of CD19(+)CD5(+) B cells. CD19(+)CD5(+)Foxp3(+) B cells showed spontaneous apoptosis. Rare CD19(+)CD5(+) Foxp3(+) regulatory B cell (Breg) population was unveiled in human peripheral blood mononuclear cells and suggested as possible regulatory B cells (Breg) as regulatory T cells (Treg). The immunologic and the clinical relevant of Breg needs to be further investigated.

• IMMUNE NETWORK
• /
• v.9 no.6
• /
• pp.209-235
• /
• 2009

There has been an explosion of literature focusing on the role of regulatory T (Treg) cells in cancer immunity. It is becoming increasingly clear that Treg cells play an active and significant role in the progression of cancer, and have an important role in suppressing tumor-specific immunity. Thus, there is a clear rationale for developing clinical strategies to diminish their regulatory influences, with the ultimate goal of augmenting antitimor immunity. Therefore, manipulation of Treg cells represent new strategies for cancer treatment. In this Review, I will summarize and review the explosive recent studies demonstrating that Treg cells are increased in patients with malignancies and restoration of antitumor immunity in mice and humans by depletion or reduction of Treg cells. In addition, I will discuss both the prognostic value of Treg cells in tumor progression in tumor-bearing hosts and the rationale for strategies for therapeutic vaccination and immunotherapeutic targeting of Treg cells with drugs and microRNA.

• Genomics & Informatics
• /
• v.19 no.2
• /
• pp.16.1-16.14
• /
• 2021

Even in the current age of advanced medicine, the prognosis of malignant peritoneal mesothelioma (MPM) remains abysmal. Molecular mechanisms responsible for the initiation and progression of MPM are still largely not understood. Adopting an integrated bioinformatics approach, this study aims to identify the key genes and pathways responsible for MPM. Genes that are differentially expressed in MPM in comparison with the peritoneum of healthy controls have been identified by analyzing a microarray gene expression dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of these differentially expressed genes (DEG) were conducted to gain a better insight. A protein-protein interaction (PPI) network of the proteins encoded by the DEGs was constructed using STRING and hub genes were detected analyzing this network. Next, the transcription factors and miRNAs that have possible regulatory roles on the hub genes were detected. Finally, survival analyses based on the hub genes were conducted using the GEPIA2 web server. Six hundred six genes were found to be differentially expressed in MPM; 133 are upregulated and 473 are downregulated. Analyzing the STRING generated PPI network, six dense modules and 12 hub genes were identified. Fifteen transcription factors and 10 miRNAs were identified to have the most extensive regulatory functions on the DEGs. Through bioinformatics analyses, this work provides an insight into the potential genes and pathways involved in MPM.

• Proceedings of the Microbiological Society of Korea Conference
• /
• 2009.05a
• /
• pp.109-109
• /
• 2009

• The Korea Genome Conference
• /
• 2004.07a
• /
• pp.35.2-35.2
• /
• 2004

• Proceedings of the Zoological Society Korea Conference
• /
• 2003.08a
• /
• pp.55-55
• /
• 2003

No Abstract, See Full Text

• Proceedings of the Microbiological Society of Korea Conference
• /
• 2006.05a
• /
• pp.82-83
• /
• 2006