• Bioinformatics and Biosystems
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• v.2 no.1
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• pp.24-27
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• 2007

Human cell regulatory mechanism is one of suspicious problems among biologists. Here we tried to uncover the human breast cancer cell regulatory mechanism from gene expression data (Marc J. Van de vijver, et. al., 2002) using a module network algorithm which is suggested by Segal, et. al.(2003) Finally, we derived a module network which consists of 50 modules and 10 tree depths. Moreover, to validate this candidate network, we applied a GO enrichment test and known transcription factor-target relationships from Transfac(R) (V. Matys, et. al, 2006) and HPRD database (Peri, S. et al., 2003).

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.131-132
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• 2007

In this paper, we designed and implemented bio-signals transmission and storage system using wireless sensor network based on ZigBee. Wireless sensor network is organized with routing protocol based on tree structure. The data is transmitted to monitoring system based on SIP. ZigBee will be used as various combinations with other wireless network technologies for application purposes.

• Journal of Distribution Science
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• v.18 no.9
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• pp.45-53
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• 2020

Purpose: This study aims at investigating consumers' choice in online food purchasing behavior and the impact on repurchase for fresh food delivery which has recently shown rapid growth in Korea. The study focuses on the user experience factors af fecting satisfaction and intention to continuously use the online food market. Research design, data and methodology: The survey was conducted by 309 people who had purchased fresh food online, and the analysis was conducted using SPSS and AMOS. Structural Equation Modeling was used for the analysis for the verification of hypotheses. The factors that consumers value when ordering fresh food delivery services were defined as system quality, service quality, commodity quality, brand characteristics, and economics from the preceding study and the relationship between satisfaction and willingness to repurchase was verified. Results: When consumers purchase fresh food online, system quality, product quality, brand characteristics, and economics have had a significant impact on satisfaction. Meanwhile, of the five optional attributes of consumers, only economic efficiency has been verified to have a statistically significant impact on repurchase intentions. Conclusions: The results of the study suggested factors that consumers consider important when ordering fresh food online, providing basic data for companies to develop related strategies.

• Convergence Security Journal
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• v.18 no.3
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• pp.87-94
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• 2018

Privacy means the right not to interfere with the private life of an individual. Bio data is the most private personal information about the person itself, and according to advancement of technology, it is possible to analyze and judge individual as well as identify individual. The Personal Information Protection Act is based on global privacy principles, but the legislation for the protection of bio information has yet to be enacted. Therefore, it is time to protect biometric data as more sensitive information than general personal information. We will review the global privacy discussions for protecting biometric information and propose additional privacy principles and measures for utilization that should be defined in the biometric information protection guideline.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• Mycobiology
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• v.49 no.6
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• pp.599-603
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• 2021

CRISPR/Cas9 genome editing systems have been established in a broad range of eukaryotic species. Herein, we report the first method for genetic engineering in pyogo (shiitake) mushrooms (Lentinula edodes) using CRISPR/Cas9. For in vivo expression of guide RNAs (gRNAs) targeting the mating-type gene HD1 (LeA1), we identified an endogenous LeU6 promoter in the L. edodes genome. We constructed a plasmid containing the LeU6 and glyceraldehyde-3-phosphate dehydrogenase (LeGPD) promoters to express the Cas9 protein. Among the eight gRNAs we tested, three successfully disrupted the LeA1 locus. Although the CRISPR-Cas9-induced alleles did not affect mating with compatible monokaryotic strains, disruption of the transcription levels of the downstream genes of LeHD1 and LeHD2 was detected. Based on this result, we present the first report of a simple and powerful genetic manipulation tool using the CRISPR/Cas9 toolbox for the scientifically and industrially important edible mushroom, L. edodes.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.1749-1752
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• 2012

Colorectal cancer (CRC), now the third most common cancer across the world, is known to aggregate in families. USP7 is a very important protein with an important role in regulating the p53 pathway, which is critical for genomic stability and tumor suppression. We here genotyped eight SNPs within the USP7 gene and conducted a case-control study in 312 CRC patients and 270 healthy subjects in the Chinese Han population. No significant associations were found for any single SNP and CRC risk. Our data eliminate USP7 as a potential candidate gene towards for CRC in the Han Chinese population.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.331-340
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• 2008

Feature Selection is one of methods to improve the classification accuracy of data in the field of machine learning. Many feature selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. Bio-inspired algorithms are well-known evolutionary algorithms based on the principles of behavior of organisms, and very useful methods to find the optimal solution in optimization problems. Bio-inspired algorithms are also used in the field of feature selection problems. So in this paper we proposed new improved bio-inspired algorithms for feature selection. We used well-known bio-inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to find the optimal subset of features that shows the best performance in classification accuracy. In addition, we modified the bio-inspired algorithms considering the prior importance (prior relevance) of each feature. We chose the mRMR method, which can measure the goodness of single feature, to set the prior importance of each feature. We modified the evolution operators of GA and PSO by using the prior importance of each feature. We verified the performance of the proposed methods by experiment with datasets. Feature selection methods using GA and PSO produced better performances in terms of the classification accuracy. The modified method with the prior importance demonstrated improved performances in terms of the evolution speed and the classification accuracy.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.517-524
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• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.467-475
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• 2014

With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. A pathway is the biological deep knowledge that represents the relations of dynamics and interaction among proteins, genes and cells by a network. A pathway is wildly being used as an important part of a bio-medical big-data analysis. However, a pathway analysis requires a lot of time and effort because a pathway is very diverse and high volume. Also, multidimensional analysis systems for various pathways are nonexistent even now. In this paper, we proposed a pathway analysis system that collects user interest pathways from KEGG pathway database that supports the most widely used pathways, constructs a network based on a hierarchy structure of pathways and analyzes the relations of dynamics and interaction among pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our pathway analysis system, we evaluate the performance of our system in various experiments.