• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.475-483
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• 2015

Genes and microRNAs (miRNAs) have important roles in human oncology. However, most of the biological factors are reported in disperse form which makes it hard to discover the pathology. In this study, genes and miRNAs involved in human endometrial cancer(EC) were collected and formed into regulatory networks following their interactive relations, including miRNAs targeting genes, transcription factors (TFs) regulating miRNAs and miRNAs included in their host genes. Networks are constructed hierarchically at three levels: differentially expressed, related and global. Among the three, the differentially expressed network is the most important and fundamental network that contains the key genes and miRNAs in EC. The target genes, TFs and miRNAs are differentially expressed in EC so that any mutation in them may impact on EC development. Some key pathways in networks were highlighted to analyze how they interactively influence other factors and carcinogenesis. Upstream and downstream pathways of the differentially expressed genes and miRNAs were compared and analyzed. The purpose of this study was to partially reveal the deep regulatory mechanisms in EC using a new method that combines comprehensive genes and miRNAs together with their relationships. It may contribute to cancer prevention and gene therapy of EC.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.1-7
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• 2014

WireWireless sensor networks(WSNs) are generally comprised of densely deployed sensor nodes, which causes highly redundant sensor data transmission and energy waste. Many studies have focused on energy saving in WSNs. However, delay problem also should be taken into consideration for mission-critical applications. In this paper, we propose a BISA (Bio-Inspired Scheduling Algorithm) to reduce the energy consumption and delay for WSNs inspired by biological systems. BISA investigates energy-efficient routing path and minimizes the energy consumption and delay using multi-channel for data transmission. Through simulations, we observe that the BISA archives energy efficiency and delay guarantees.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• Genomics & Informatics
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• v.6 no.4
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• pp.210-222
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• 2008

Due to the polygenic nature of cancer, it is believed that breast cancer is caused by the perturbation of multiple genes and their complex interactions, which contribute to the wide aspects of disease phenotypes. A systems biology approach for the identification of subnetworks of interconnected genes as functional modules is required to understand the complex nature of diseases such as breast cancer. In this study, we apply a 3-step strategy for the interpretation of microarray data, focusing on identifying significantly perturbed metabolic pathways rather than analyzing a large amount of overexpressed and underexpressed individual genes. The selected pathways are considered to be dysregulated functional modules that putatively contribute to the progression of disease. The subnetwork of protein-protein interactions for these dysregulated pathways are constructed for further detailed analysis. We evaluated the method by analyzing microarray datasets of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Using the strategy of microarray analysis, we selected several significantly perturbed pathways that are implicated in the regulation of progression of breast cancers, including the extracellular matrix-receptor interaction pathway and the focal adhesion pathway. Moreover, these selected pathways include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting interesting perturbed pathways that putatively play a role in the progression of breast cancer and provides an improved interpretability of networks of protein-protein interactions.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.796-804
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• 2006

The objective of this research was to develop ecological multi-metric models using natural fish assemblages for a diagnosis of current stream health condition, and apply the model to nationwide lotic ecosystems of the Geum River, the Youngsan River, and the Sumjin River. The ecological stream health model was based on the index of biological integrity (IBI), which was originally developed in North American streams by Karr (1981), and the Rapid Bioassessment Protocol (RBP), which was scientifically established by the US EPA (1999). The metric numbers and metric attributes were partially changed for the regional applications, so the scoring criteria was modified for the assessment. Overall, metric values, based on the IBI calculations, reflected conventional water quality characteristics, based on nutrient regime, and agreed with results of staticeco-toxicity tests. Some stations impaired in terms of stream health were identified by the IBI approach, and also major key stressors affecting the stream health were identified by additional evaluations of physical habitats. Our preliminary results suggested that biological integrity in stream ecosystems was largely disturbed by habitat degradation as well as chemical pollutions. This new approach would be used as a key tool for ecological restorations and species conservations in the degraded aquatic ecosystems in Korea and applied for elucidating major causes of ecological disturbances. Ultimately, this approach provides us an effective management strategy of stream ecosystems through establishments of ecological networks in various watersheds.

• Journal of Adhesion and Interface
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• v.16 no.1
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• pp.15-21
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• 2015

In this study, rheological properties and flow dynamics in roll coating process of basecoat paints have been investigated for automotive precoated metal (PCM) sheet applications. Various rheological properties for basecoats with three colors (black, blue, and silver), such as shear viscosity data at room temperature and elastic/viscous moduli under thermal curing condition, have been measured using a rotational rheometer. It is found that the relative portion of function groups inside basecoats and their viscosity level have greatly affected the formation of crosslinked networks by thermal curing. Also, operability coating windows for basecoats have been established in three-roll coating process system by observing their flow instabilities such as ribbing and cascade. It is confirmed that rheological approaches applied in this study have been usefully applied to develop environmentally-friendly PCM coating technology and optimally control the coating operations for non-Newtonian PCM paints.

• Journal of Ginseng Research
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• v.47 no.1
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• pp.54-64
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• 2023

Background: Panax ginseng Meyer (P. ginseng) is a traditional natural/herbal medicine. The amelioration on inflammatory bowel disease (IBD) activity rely mainly on its main active ingredients that are referred to as ginsenosides. However, the current literature on gut microbiota, gut microbiota-host co-metabolites, and systems pharmacology has no studies investigating the effects of ginsenoside on IBD. Methods: The present study was aimed to investigate the role of ginsenosides and the possible underlying mechanisms in the treatment of IBD in an acetic acid-induced rat model by integrating metagenomics, metabolomics, and complex biological networks analysis. In the study ten ginsenosides in the ginsenoside fraction (GS) were identified using Q-Orbitrap LC-MS. Results: The results demonstrated the improvement effect of GS on IBD and the regulation effect of ginsenosides on gut microbiota and its co-metabolites. It was revealed that 7 endogenous metabolites, including acetic acid, butyric acid, citric acid, tryptophan, histidine, alanine, and glutathione, could be utilized as significant biomarkers of GS in the treatment of IBD. Furthermore, the biological network studies revealed EGFR, STAT3, and AKT1, which belong mainly to the glycolysis and pentose phosphate pathways, as the potential targets for GS for intervening in IBD. Conclusion: These findings indicated that the combination of genomics, metabolomics, and biological network analysis could assist in elucidating the possible mechanism underlying the role of ginsenosides in alleviating inflammatory bowel disease and thereby reveal the pathological process of ginsenosides in IBD treatment through the regulation of the disordered host-flora co-metabolism pathway.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.254-257
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• 2002

In previous study about combinatorial optimization problem solver by using neural network, since Hopfield method, to converge into the optimum solution sooner and certainer is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, the dynamical system has lately attracted attention. Then we propose the "dynamical" combinatorial optimization problem solver using hysteresis neural network. In this article, the proposal system is evaluated by the N-Queen problem.