• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.164-169
• /
• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Genomics & Informatics
• /
• v.4 no.1
• /
• pp.33-39
• /
• 2006

Alternative splicing (AS) is an important mechanism of producing transcriptome diversity and microarray techniques are being used increasingly to monitor the splice variants. There exist three types of microarrays interrogating AS events-junction, exon, and tiling arrays. Junction probes have the advantage of monitoring the splice site directly. Johnson et al., performed a genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays (Science 302:2141-2144, 2003), which monitored splicing at every known exon-exon junctions for more than 10,000 multi-exon human genes in 52 tissues and cell lines. Here, we describe an algorithm to deduce the relative concentration of isoforms from the junction array data. Non-negative Matrix Factorization (NMF) is applied to obtain the transcript structure inferred from the expression data. Then we choose the transcript models consistent with the ECgene model of alternative splicing which is based on mRNA and EST alignment. The probe-transcript matrix is constructed using the NMF-consistent ECgene transcripts, and the isoform abundance is deduced from the non-negative least squares (NNLS) fitting of experimental data. Our method can be easily extended to other types of microarrays with exon or junction probes.

• Toxicological Research
• /
• v.30 no.4
• /
• pp.283-289
• /
• 2014

Tea flavonoids and polyphenols are well known for their extraordinary antioxidant activity which is considered important for anti-aging processes in animals. This study evaluated the anti-wrinkle effects of three different kinds of tea (Camellia sinensis) water extracts (CSWEs) including green, white, and black teas using a photoaged hairless mouse model. Data showed that the CSWE-treatment greatly improved skin conditions of mice suffering from UVB-induced photoaging, based on the parameters including the skin erythema index, moisture capacity, and transepidermal water loss. In addition, the wrinkle measurement and image analysis of skin replicas indicated that CSWEs remarkably inhibited wrinkle formation. In histological examination, the CSWE-treated mice exhibited diminished epidermal thickness and increased collagen and elastic fiber content, key signatures for skin restoration. Furthermore, the reduced expression of MMP-3, a collagen-degradative enzyme, was observed in the skin of CSWE-treated animals. Interestingly, comparative data between green, white, and black tea indicated that the anti-wrinkle activity of white tea and black tea is equally greater than that of green tea. Taken together, these data clearly demonstrated that CSWEs could be used as an effective anti-wrinkle agent in photoaged animal skin, implying their extended uses in therapeutics.

• BMB Reports
• /
• v.38 no.2
• /
• pp.243-247
• /
• 2005

Dishevelled (Dvl) is a positive regulator of the canonical Wnt signaling pathway, which regulates the levels of $\beta$-catenin. The $\beta$-catenin oncoprotein depends upon the association of Dvl and Axin proteins through their DIX domains, and its accumulation directs the expression of specific developmental-related genes at the nucleus. Here, the $^1H$, $^{13}C$, and $^{15}N$ resonances of the human Dishevelled 2 DIX domain are assigned using heteronuclear nuclear magnetic resonance (NMR) spectroscopy. In addition, helical and extended elements are identified based on the NMR data. The results establish a structural context for characterizing the actin and phospholipid interactions and binding sites of this novel domain, and provide insights into its role in protein localization to stress fibers and cytoplasmic vesicles during Wnt signaling.

• Korean Journal of Computational Design and Engineering
• /
• v.11 no.6
• /
• pp.393-402
• /
• 2006

The purpose of this study is to develop mechanisms of nD model-based design by the combination of 2D drawing standards and 3D building models from the current 2D and text-based design. The aim of this study can be archived by defining the 2D model extension definitions for the IFC model development and harmonizing existing 2D standards. The paper examines 1) 3D Representation of Building Element and Building Services element, and 2D Model extension of IFC2X.2, 2) Basic development of additional 2D element that should be added to IFC model, and 3) mapping method between current 2D standard and IFC2.X2. Following this approach, the interoperability problem between 3D model and 2D drawing can be solved and finally an extended data model could be developed.

• Science of Emotion and Sensibility
• /
• v.24 no.2
• /
• pp.75-80
• /
• 2021

Do happy applicants achieve more? Although it is well established that happiness predicts desirable work-related outcomes, previous findings were primarily obtained in social settings. In this study, we extended the scope of the "happiness premium" effect to the artificial intelligence (AI) context. Specifically, we examined whether an applicant's happiness signal captured using an AI system effectively predicts his/her objective performance. Data from 3,609 job applicants showed that verbally expressed happiness (frequency of positive words) during an AI interview predicts cognitive task scores, and this tendency was more pronounced among women than men. However, facially expressed happiness (frequency of smiling) recorded using AI could not predict the performance. Thus, when AI is involved in a hiring process, verbal rather than the facial cues of happiness provide a more valid marker for applicants' hiring chances.

• The Korean Journal of Physiology and Pharmacology
• /
• v.25 no.5
• /
• pp.395-401
• /
• 2021

Extended inflammation and cytokine production pathogenically contribute to a number of inflammatory disorders. Formosanin C (FC) is the major diosgenin saponin found in herb Paris formosana Hayata (Liliaceae), which has been shown to exert anti-cancer and immunomodulatory functions. In this study, we aimed to investigate anti-inflammatory activity of FC and the underlying molecular mechanism. RAW264.7 macrophages were stimulated with lipopolysaccharide (LPS) or pretreated with FC prior to being stimulated with LPS. Thereafter, the macrophages were subjected to analysis of the expression levels of pro-inflammatory mediators, including nitric oxide (NO), prostaglandin E₂ (PGE), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6, as well as two relevant enzymes, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2). The analysis revealed that FC administration blunted LPS-induced production of NO and PGE in a dose-dependent manner, while the expression of iNOS and COX-2 at both mRNA and protein levels was inhibited in LPS-stimulated macrophages pre-treated with FC. Moreover, LPS stimulation upregulated mRNA expression and medium release of TNF-α, IL-1β, and IL-6, whereas this effect was blocked upon FC pre-administration. Mechanistic studies showed that inhibitory effects of FC on LPS-induced inflammation were associated with a downregulation of IκB kinase, IκB, and p65/NF-κB pathway. Taken together, these data suggest that FC possesses an inflammation-suppressing activity, thus being a potential agent for the treatment of inflammation-associated disorders.

• Journal of the Korea Convergence Society
• /
• v.11 no.3
• /
• pp.1-7
• /
• 2020

This paper introduces two different techniques for dealing with incomplete data and algorithms for learning this data. The first method is to process the incomplete data by assigning the missing value with equal probability that the missing variable can have, and learn this data with the SVM. This technique ensures that the higher the frequency of missing for any variable, the higher the entropy so that it is not selected in the decision tree. This method is characterized by ignoring all remaining information in the missing variable and assigning a new value. On the other hand, the new method is to calculate the entropy probability from the remaining information except the missing value and use it as an estimate of the missing variable. In other words, using a lot of information that is not lost from incomplete learning data to recover some missing information and learn using deep learning. These two methods measure performance by selecting one variable in turn from the training data and iteratively comparing the results of different measurements with varying proportions of data lost in the variable.

• Journal of the Korea Convergence Society
• /
• v.10 no.2
• /
• pp.1-6
• /
• 2019

The proposed model is developed to minimize the loss of information in incomplete data including missing data. The first step is to transform the learning data to compensate for the loss information using the data extension technique. In this conversion process, the attribute values of the data are filled with binary or probability values in one-hot encoding. Next, this conversion data is input to the deep learning model, where the number of entries is not constant depending on the cardinality of each attribute. Then, the entry values of each attribute are assigned to the respective input nodes, and learning proceeds. This is different from existing learning models, and has an unusual structure in which arbitrary attribute values are distributedly input to multiple nodes in the input layer. In order to evaluate the learning performance of the proposed model, various experiments are performed on the missing data and it shows that it is superior in terms of performance. The proposed model will be useful as an algorithm to minimize the loss in the ubiquitous environment.

• Genomics & Informatics
• /
• v.10 no.2
• /
• pp.69-73
• /
• 2012

The explosive development of genomics technologies including microarrays and next generation sequencing (NGS) has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.