• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.830-841
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• 2022

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.161-164
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• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• Genomics & Informatics
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• v.16 no.4
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• pp.36.1-36.3
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• 2018

Next generation sequencing (NGS), a high-throughput DNA sequencing technology, is widely used for molecular biological studies. In NGS, RNA-sequencing (RNA-Seq), which is a short-read massively parallel sequencing, is a major quantitative transcriptome tool for different transcriptome studies. To utilize the RNA-Seq data, various quantification and analysis methods have been developed to solve specific research goals, including identification of differentially expressed genes and detection of novel transcripts. Because of the accumulation of RNA-Seq data in the public databases, there is a demand for integrative analysis. However, the available RNA-Seq data are stored in different formats such as read count, transcripts per million, and fragments per kilobase million. This hinders the integrative analysis of the RNA-Seq data. To solve this problem, we have developed a web-based application using Shiny, COEX-seq (Convert a Variety of Measurements of Gene Expression in RNA-Seq) that easily converts data in a variety of measurement formats of gene expression used in most bioinformatic tools for RNA-Seq. It provides a workflow that includes loading data set, selecting measurement formats of gene expression, and identifying gene names. COEX-seq is freely available for academic purposes and can be run on Windows, Mac OS, and Linux operating systems. Source code, sample data sets, and supplementary documentation are available as well.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.135-141
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• 2015

The objective of this study is to develop an early warning system for rainfall induced landslides. For this study, we suggested an analysis process using rainfall forecast data. 1) For a selected slope, safety factor with saturated depth was analyzed and safety factor threshold was established (warning FS threshold=1.3, alarm FS threshold=1.1). 2) If rainfall started, saturated depth and safety factor was calculated with rainfall forecast data, 3) And every hour after safety factor is compared with threshold, then warning or alarm can issued. In the future, we plan to make a early warning system combined with the in-situ inclinometer sensors.

• Genomics & Informatics
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• v.3 no.3
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• pp.80-85
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• 2005

HExDB is a database for analyzing exon and splicing pattern information in Homo sapiens. HExDB is useful for specific purposes: 1) to design primers for exon amplification from cDNA and 2) to understand the change of ORFs by alternative splicing. HExDB was constructed by integrating data from AltExtron which is the computationally predicted exon database, Ensemble cDNA annotation, and Affymetrix genome tile published recently. Although it may contain false positive data, HExDB is good starting point due to its sensitivity. At present, there areas many as 2,046,519 exons stored in the HExDB. We found that $16.8\%$ of the exons in the database was constitutive exons and $83.1\%$ were novel gene exons.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.171-179
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• 2015

The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.1-9
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• 2010

Recently, 3D stereoscopic display such as 3D stereoscopic cinemas and 3D stereoscopic TV is getting a lot of interest. In general, a stereo image can be used in 3D stereoscopic display. In other hands, for 3D auto stereoscopic display, the elemental images should be generated through visualization from every camera in a lens array. Since a lens array consists of several cameras, it takes a lot of time to generate the elemental images with respect to 3D virtual space, specially, if a large bio-medical volume data is in the 3D virtual space, it will take more time. In order to improve the problem, in this paper, we construct an octree for a given bio-medical volume data and then propose a method to generate the elemental images through efficient rendering of the Octree data using GPU. Experimental results show that the proposed method can obtain more improvement comparable than conventional one, but the development of more efficient method is required.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.173-173
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• 2017

드론의 시장규모가 커짐에 따라 초창기 군사 목적에서 현재 민간부문으로 확대되고 있다. 현재 드론은 실외에서 사용될 목적으로 제작된 것이 많으나 실내에서도 드론의 활용 여부가 증가할 것으로 예상된다. 본 연구에서는 실외에서만 사용 가능한 GPS를 대신하여 영상 촬영으로 획득한 이미지를 CNN으로 학습을 시켜 자율고도제어비행을 하도록 한다. 첫 번째로 수동 조작하는 드론에 IMU센서를 부착하여 획득한 고도 데이터를 표로 제시함으로써 GPS를 사용하지 않는 드론의 실내주행에서 일정한 고도 유지는 다소 무리가 있음을 보여준다. 두 번째로 드론의 수동 조작은 일정하지 않은 고도 때문에 CNN의 학습할 영상 획득이 어렵다. 일정한 고도의 영상 획득을 위한 실험용 높이 조절 Base를 제작하여 고도별 영상을 획득한다. 획득한 영상을 통해 얻은 이미지를 CNN 학습을 시킨 후, 학습에 사용되지 않은 이미지를 사용하여 고도 판별을 확인한다. 대조군으로 실내장소를 바꾸어 미리 학습된 CNN으로 고도 판별을 확인한다. 학습에 사용된 이미지의 환경(생명공학관)과 대조군(제 2 공학관)이 촬영된 장소의 환경요소의 차이로 오차가 발생한다. 오차는 실내 장소의 총 높이의 차이 및 서로 상이한 천장 구조물에 따른 것으로 사료되며 Data crop을 통해 획득한 이미지의 천정 부분을 제거하여 노이즈를 줄여 고도 판별의 정확도를 높일 수 있을 것으로 예상한다. 세 번째, CNN으로 학습을 통해 Model을 도출하여 자율 고도 제어 프로세스를 제시한다. 그리고 해당 프로세스를 이용한 자율고도제어 주행과 수동조작을 통한 주행에서의 Z축 가속도 데이터의 표준편차를 비교하여 본 연구의 실효성을 보여준다

• Bulletin of the Korean Chemical Society
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• v.32 no.8
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• pp.2717-2721
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• 2011

Peroxisome proliferator-activated receptors (PPARs) are a subfamily of nuclear receptors (NRs). Human peroxisome proliferator-activated receptor gamma (hPPAR${\gamma}$) has been implicated in the pathology of numerous diseases, including obesity, diabetes, and cancer. ELISA-based hPPAR${\gamma}$ activation assay showed that biapigenin increased the binding between hPPAR${\gamma}$ and steroid receptor coactivator-1 (SRC-1) by approximately 3-fold. In order to confirm that biapigenin binds to hPPAR${\gamma}$, fluorescence quenching experiment was performed. The results showed that biapigenin has higher binding affinity to hPPAR${\gamma}$ at nanomolar concentrations compared to indomethacin. Biapigenin showed anticancer activity against HeLa cells. Biapigenin was noncytotoxic against HaCa T cell. All these data implied that biapigenin may be a potent agonist of hPPAR${\gamma}$ with anticancer activity. We will further investigate its anticancer effects against human cervical cancer.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.348-348
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• 2017

This experiment was conducted to investigate the aspect of tiller occurrence, growth and yield of sorghum according to planting density and sowing date. The subject of this experiment is to supply basic data to inhibit non-productive tillers uneconomical and cumbersome for mechanical harvesting. Also another subject was to evaluate optimum planting density and sowing date in central district area. Total number of tillers was more in 80cm ridge than 60cm ridge and it was increased as the planting distance was wider from 15cm to 30cm on the each ridge. Ratio of effective tillers was higher in 60cm ridge than 80cm ridge and it was decreased as planting distance was wider from 15cm to 30cm. The lower the planting density, the more increased total number of tillers, whereas effective tillers were decreased as planting density was high. Average of total number of tillers of three varieties was higher in sowing date of 2 May (1st sowing date), whereas ratio of effective tillers was the highest in sowing date of 23 May (2nd sowing date). Hwanggeumchal showed the highest total number of tillers (1.2 tillers), while Moktaksusu had the lowest total number of tillers (0.8 tillers) among three varieties. There were no significant difference between planting density and days to heading and ripening date from seeding. Culm length increased as planting density was high, but ear length, grains per ear and 1000 grain weight were decreased on the other hand. The highest yield of sorghum per 10a was obtained from $60{\times}20cm$ planting density among 6 planting densities.