• Genomics & Informatics
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• v.21 no.3
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• pp.37.1-37.11
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• 2023

Systemic lupus erythematosus (SLE) is an inflammatory-autoimmune disease with a complex multi-organ pathogenesis, and it is known to be associated with significant morbidity and mortality. Various genetic, immunological, endocrine, and environmental factors contribute to SLE. Genomic variants have been identified as potential contributors to SLE susceptibility across multiple continents. However, the specific pathogenic variants that drive SLE remain largely undefined. In this study, we sought to identify these pathogenic variants across various continents using genomic and bioinformatic-based methodologies. We found that the variants rs35677470, rs34536443, rs17849502, and rs13306575 are likely damaging in SLE. Furthermore, these four variants appear to affect the gene expression of NCF2, TYK2, and DNASE1L3 in whole blood tissue. Our findings suggest that these genomic variants warrant further research for validation in functional studies and clinical trials involving SLE patients. We conclude that the integration of genomic and bioinformatic-based databases could enhance our understanding of disease susceptibility, including that of SLE.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• Korean Journal of Breeding Science
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• v.43 no.2
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• pp.103-114
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• 2011

In recent years, genomic resources and information have accumulated at an ever increasing pace, in many plant species, through whole genome sequencing, large scale analysis of transcriptomes, DNA markers and functional studies of individual genes. Well-characterized species within key plant taxa, co-called "model systems", have played a pivotal role in nucleating the accumulation of genomic information and databases, thereby providing the basis for comparative genomic studies. In addition, recent advances to "Next Generation" sequencing technologies have propelled a new wave of genomics, enabling rapid, low cost analysis of numerous genomes, and the accumulation of genetic diversity data for large numbers of accessions within individual species. The resulting wealth of genomic information provides an opportunity to discern evolutionary processes that have impacted genome structure and the function of genes, using the tools of comparative analysis. Comparative genomics provides a platform to translate information from model species to crops, and to relate knowledge of genome function among crop species. Ultimately, the resulting knowledge will accelerate the development of more efficient breeding strategies through the identification of trait-associated orthologous genes and next generation functional gene-based markers.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.57
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• pp.11-20
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• 2000

In dynamic management environment, CEO must make an efficient decision with information & knowledge management systems based on IT(Information Technology). As a key component to cope with this current, we suggest the business performance analysis system based on KDD(Knowledge Discovery in Databases). We consider the theoretical model that is composited both Value-Added in respect of stakeholder and Economic Value-Added in respect of shareholder. Additionally we use DBMS and data mining method using Genetic Algorithms as physical model. To demonstrate the performance of the business performance analysis system, we analyse a domestic motors industry. The empirical case is based on the financial data of KISFAS(Korea Investors Services Financial Analysis System) database. The samples included in the study consist of H motors/S motors industry over the 16-year from 1981 to 1996.

• IE interfaces
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• v.14 no.2
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• pp.164-171
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• 2001

We suggest a Business Performance Information System with Knowledge Discovery in Databases(KDD) as a key component of integrated information and knowledge management system. The proposed system measures business performance by considering both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. In modeling of Business Performance Information System, we apply the following KDD processes : Data Warehouse for consistent management of a performance data, On-Line Analytic Processing(OLAP) for multidimensional analysis, Genetic Algorithms for exploring and finding dominant managing factors and Analytic Hierarchy Process(AHP) for applying expert's knowledge and experience. To demonstrate the performance of the system, we conducted a case study using financial data of Korean automobile industry over 16 years from 1981 to 1996, which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2702-2719
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• 2013

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.10
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• pp.1394-1398
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• 2014

Indigenous (native) breeds of livestock have higher disease resistance and adaptation to the environment due to high genetic diversity. Even though their extinction rate is accelerated due to the increase of commercial breeds, natural disaster, and civil war, there is a lack of well-established databases for the native breeds. Thus, we constructed the native pig and chicken breed database (NPCDB) which integrates available information on the breeds from around the world. It is a nonprofit public database aimed to provide information on the genetic resources of indigenous pig and chicken breeds for their conservation. The NPCDB (http://npcdb.snu.ac.kr/) provides the phenotypic information and population size of each breed as well as its specific habitat. In addition, it provides information on the distribution of genetic resources across the country. The database will contribute to understanding of the breed's characteristics such as disease resistance and adaptation to environmental changes as well as the conservation of indigenous genetic resources.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7149-7154
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• 2013

Objective: Increasing scientific evidence suggests that common variants in the PALB2 gene may confer susceptibility to breast cancer, but many studies have yielded inconclusive results. This meta-analysis aimed to derive a more precise estimation of the relationship between PALB2 genetic variants and breast cancer risk. Methods: An extensive literary search for relevant studies was conducted in PubMed, Embase, Web of Science, Cochrane Library, CISCOM, CINAHL, Google Scholar, CNKI and CBM databases from their inception through September 1st, 2013. A meta-analysis was performed using the STATA 12.0 software and crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Six case-control studies were included with a total of 4,499 breast cancer cases and 6,369 healthy controls. Our meta-analysis reveals that PALB2 genetic variants may increase the risk of breast cancer (allele model: OR>1.36, 95%CI: 1.20~1.52, P < 0.001; dominant model: OR>1.64, 95%CI: 1.42~1.91, P < 0.001; respectively). Subgroup analyses by ethnicity indicated PALB2 genetic variants were associated with an increased risk of breast cancer among both Caucasian and Asian populations (all P < 0.05). No publication bias was detected in this meta-analysis (all P > 0.05). Conclusion: The current meta-analysis indicates that PALB2 genetic variants may increase the risk of breast cancer. Thus, detection of PALB2 genetic variants may be a promising biomarker approach.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.10a
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• pp.74-74
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• 2018

Little millet (Panicum sumatrense) is well known for its salt and drought stress tolerance and high nutritional value, but very limited knowledge of genetic variation and genomic information is available. This study was to develop highly polymorphic EST-SSR markers based on cross-species transferability of derived SSRs from switchgrass EST databases and characterize newly developed EST - SSRs to better understand the genetic diversity of collected 37 germplasm accessions of little millet. A total of 779 primer pairs were designed from the 22,961 EST sequences of switchgrass (Pancium virgatum), of which 48 EST - SSR markers were developed based on the trials of transferability of these primers in little millet. The EST - SSR amplicons showed reproducible single band polymorphism and produced a total of 160 alleles with an average of 3.3 alleles per locus in 37 accessions of little millet. T he average values of expected and observed heterozygosities were 0.266 and 0.123, respectively. T he polymorphic information content (PIC) values were observed in range of 0.026 to 0.549 with an average of 0.240. The genetic relatedness among the little millet accessions was evaluated by neighbor-joining dendrogram, which grouped all accessions into two distinct groups. The validation thus demonstrated the utility of the switchgrass EST - SSR markers in assessing genomic relationships in little millet. T he findings from this study could be useful for designing strategies for the identification of diverse germplasm for conservation and future molecular breeding programs for little millet.