• 한국컴퓨터정보학회논문지
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• 제14권6호
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• pp.125-134
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• 2009

웹을 통한 생물 데이터 접근 방식은 많은 과학자들에게 대화식으로 서로 다른 형식의 생물 데이터베이스 내용을 검색할 뿐만 아니라, 한 데이터베이스에서 다른 분자생물 데이터베이스로의 연결을 위한 강력한 도구를 제공한다. 본 논문에서의 생물 개념 모델은 생물 데이터 제어를 위한 4가지 통합 레이어를 기반으로 각 생물 데이터 소스 간의 연관성에 따른 규칙 속성을 적용하고 데이터 소스 중에 관심 대상이 되는 개체를 표현하여 하이브리드 생물 데이터 모델을 구성하였다. 특정 사용자의 응용 서비스 요구가 발생하면 해당 생물 데이터베이스와 웹 서비스를 통한 데이터 소스로부터 정보를 획득한다. 본 논문에서는 통합 레이어를 기반으로 웹 데이터 소스 상에서 정보를 탐색하기 위해 메타 규칙을 적용한 질의어 처리 모형과 수행구조를 정형화하였다.

• Genomics & Informatics
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• 제21권1호
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• pp.12.1-12.8
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• 2023

A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

• Genomics & Informatics
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• 제15권1호
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• pp.19-27
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• 2017

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

• Genomics & Informatics
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• 제10권4호
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• pp.263-265
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• 2012

We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

• Journal of Ecology and Environment
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• 제45권4호
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• pp.313-327
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• 2021

Background: Citizen science is becoming a mainstream approach of baseline data collection to monitor biodiversity and climate change. Dragonflies (Odonata) have been ranked as the highest priority group in biodiversity monitoring for global warming. Ischnura senegalensis Rambur has been designated a biological indicator of climate change and is being monitored by the citizen science project "Korean Biodiversity Observation Network." This study has been performed to understand changes in the distribution range of I. senegalensis in response to climate change using citizen science data in South Korea. Results: We constructed a dataset of 397 distribution records for I. senegalensis, ranging from 1980 to 2020. The number of records sharply increased over time and space, and in particular, citizen science monitoring data accounted for the greatest proportion (58.7%) and covered the widest geographical range. This species was only distributed in the southern provinces until 2010 but was recorded in the higher latitudes such as Gangwon-do, Incheon, Seoul, and Gyeonggi-do (max. Paju-si, 37.70° latitude) by 2020. A species distribution model showed that the annual mean temperature (Bio1; 63.2%) and the maximum temperature of the warmest month (Bio5; 16.7%) were the most critical factors influencing its distribution. Future climate change scenarios have predicted an increase in suitable habitats for this species. Conclusions: This study is the first to show the northward expansion in the distribution range of I. senegalensis in response to climate warming in South Korea over the past 40 years. In particular, citizen science was crucial in supplying critical baseline data to detect the distribution change toward higher latitudes. Our results provide new insights on the value of citizen science as a tool for detecting the impact of climate change on ecosystems in South Korea.

• Molecules and Cells
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• 제44권11호
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• ALGAE
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• 제31권2호
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• pp.137-154
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• 2016

Next generation sequencing (NGS) technologies have revolutionized many areas of biological research due to the sharp reduction in costs that has led to the generation of massive amounts of sequence information. Analysis of large genome data sets is however still a challenging task because it often requires significant computer resources and knowledge of bioinformatics. Here, we provide a guide for an uninitiated who wish to analyze high-throughput NGS data. We focus specifically on the analysis of organelle genome and metagenome data and describe the current bioinformatic pipelines suited for this purpose.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1994년도 춘계학술대회
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• pp.53-56
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• 1994

We have developed biological signal measurement modules and data acquisition and control card for a biological signal measurement, archiving, and communication system (SiMACS). Biological signals included in this system are ECG, EEG, EMG, invasive blood pressure, respiration, and temperature. Parameters of each module can be controlled by PC-base IDPU (intelligent data processing unit) through a data acquisition and control card. The data acquisition and control card can collect up to 16 channels of biological signals with sampling rate of $50\;{\sim}\;2,000Hz$ and 12-bit resolution. All measurement moduls and data acquisition functions are controlled by microcontroller which receives commands from PC. All data transfers among PC, microcontroller, and ADC are done through a shared RAM access by polling method for real rime operation.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제5권2호
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• pp.43-49
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• 2024

Ecological monitoring provides indispensable data for biodiversity conservation and sustainable resource management. However, the complexity and variability inherent in ecological monitoring data necessitate robust verification processes to ensure data integrity. This study employed Benford's Law, a statistical principle traditionally used in fields such as finance and health sciences, to evaluate the authenticity of ecological monitoring data related to the abundance of migratory bird species across various locations in South Korea. Benford's Law anticipates a specific logarithmic distribution of leading digits in naturally occurring numerical datasets. Our investigation involved two stages of analysis: a first-order analysis considering the leading digit and a second-order analysis examining the first two digits of bird population counts. While the first-order analysis displayed moderate conformity to Benford's Law that suggested overall data integrity, the second-order analysis revealed more pronounced deviations, indicating potential inconsistencies or inaccuracies in certain subsets of the data. Although our data did not perfectly align with Benford's Law, these deviations underscore the complex nature of ecological research, which is influenced by a multitude of environmental, methodological, and human factors.

• 한국시뮬레이션학회논문지
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• 제19권2호
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• pp.81-89
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• 2010

생물학 데이터 마이닝은 생물학 데이터의 볼륨이 급격하게 증가함에 따라 최근 주목받고 있다. 그리드 기술은 계산 자원과 데이터 공유와 활용을 가능하게 한다. 이 논문에서는 생물학 데이터 마이닝과 그리드 기술을 결합한 혼합형 시스템을 제안한다. 특히, 생물학 데이터 마이닝의 처리 효율성을 위해 결정 범위 조정 알고리즘을 사용한다. 우리는 이 알고리즘을 통해 빠르고 자동으로 신뢰할 만한 데이터 마이닝 인식률을 얻는다. 게다가 그리드 환경에서는 지리적으로 분산된 자원들을 연동하기 때문에 통신량과 자원 할당이 이슈가 된다. 우리는 동적 로드 밸런싱을 제안하고 그리드 기반 생물학 데이터 마이닝 기법에 적용한 다. 성능 평가를 위해 우리는 평균 처리 시간, 평균 통신 시간, 평균 자원 활용도를 측정한다. 측정 실험의 결과는 제안된 두 알고리즘을 적용한 우리의 기법이 처리 시간과 비용 측면에서 이점을 제공한다는 것을 보여준다.