• Genomics & Informatics
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• 제12권2호
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• pp.76-78
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• 2014

Owing to the generation of vast amounts of sequencing data by using cost-effective, high-throughput sequencing technologies with improved computational approaches, many putative proteins have been discovered after assembly and structural annotation. Putative proteins are typically annotated using a functional annotation system that uses extant databases, but the expansive size of these databases often causes a bottleneck for rapid functional annotation. We developed SFannotation, a simple and fast functional annotation system that rapidly annotates putative proteins against four extant databases, Swiss-Prot, TIGRFAMs, Pfam, and the non-redundant sequence database, by using a best-hit approach with BLASTP and HMMSEARCH.

• Genomics & Informatics
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• 제11권4호
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제24권1호
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• pp.1-6
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• 2021

Next-generation sequencing (NGS) technologies have changed the process of genetic diagnosis from a gene-by-gene approach to syndrome-based diagnostic gene panel sequencing (DPS), diagnostic exome sequencing (DES), and diagnostic genome sequencing (DGS). A priori information on the causative genes that might underlie a genetic condition is a prerequisite for genetic diagnosis before conducting clinical NGS tests. Theoretically, DPS, DES, and DGS do not require any information on specific candidate genes. Therefore, clinical NGS tests sometimes detect disease-related pathogenic variants in genes underlying different conditions from the initial diagnosis. These clinical NGS tests are expensive, but they can be a cost-effective approach for the rapid diagnosis of rare disorders with genetic heterogeneity, such as the glycogen storage disease, familial intrahepatic cholestasis, lysosomal storage disease, and primary immunodeficiency. In addition, DES or DGS may find novel genes that that were previously not linked to human diseases.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.45-52
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• 2021

The surveillance camera market has developed and plays an important role in the field of video surveillance. However, in recent years, the identification of areas requiring surveillance has been limited by reflective light in the surveillance camera market. Cameras using polarization filters are being developed to reduce reflective light and facilitate identification. Programs are required to automatically adjust polarization filters. In this paper, we proposed an optimal angle extraction method from surveillance cameras using polarization filters through histogram analysis. First of all, transformed to grayscale to analyze the specifications of frames in multiple polarized angle images, reducing computational throughput. Then we generated and analyzed a histogram of the corresponding frame to extract the angle when the highlights are the fewest. Experiments with 0˚ and 90˚ showed high performance in extracting optimal angles. At this point, it is hoped this technology would be used for surveillance cameras in place like beach with a lot of reflective light.

• The Plant Pathology Journal
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• 제38권5호
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• pp.503-512
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• 2022

Lilies (Lilium spp.) are one of the most important ornamental flower crops grown in Korea. Most viral diseases in lilies are transmitted by infected bulbs, which cause serious economic losses due to reduced yields. Various diagnostic techniques and high-throughput sequencing methods have been used to detect lily viruses. According to Oxford Nanopore Technologies (ONT), MinION is a compact and portable sequencing device. In this study, three plant viruses, lily mottle, lily symptomless, and plantago asiatica mosaic virus, were detected in lily samples using the ONT platform. As a result of genome assembly of reads obtained through ONT, 100% coverage and 90.3-93.4% identity were obtained. Thus, we show that the ONT platform is a promising tool for the diagnosis and characterization of viruses that infect crops.

• Genomics & Informatics
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• 제10권1호
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• 한국통신학회논문지
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• 제39B권12호
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• pp.817-821
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• 2014

무선 이동 통신 기술의 급속한 발달과 스마트 기기의 폭발적 보급으로 사용자들이 시간과 장소에 구애받지 않고 자유롭게 콘텐츠 생성과 공유를 시도함에 따라, CCN과 같은 콘텐츠 중심의 새로운 네트워킹 방식이 등장하게 되었다. CCN은 일대일 전송 구조를 근간으로 하고 있어 네트워크 토폴로지 변화가 빈번한 ad-hoc 환경에서는 많은 오버헤드 발생 및 낮은 전송 효율을 갖게 된다. 이에 본 논문에서는 ad-hoc 네트워킹 환경에 적합하도록 CCN 그룹 콘텐츠 전송방식을 제안한다. 성능 평가를 통해 제안 방식이 기존 방식 대비 제어 메시지 오버헤드 감소 및 전송 효율을 크게 향상시킴을 확인하였다.

• 한국산업융합학회 논문집
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• 제27권3호
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• pp.601-607
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• 2024

The discharge of oily wastewater into water bodies and soil poses a serious hazard to the environment and public health. Various conventional techniques have been employed to treat oil-water mixtures and emulsions; Unfortunately, these approaches are frequently expensive, time-consuming, and unsatisfactory outcomes. Porous materials and adsorbents are commonly used for purification, but their use is limited by low separation efficiencies and the risk of secondary contamination. Recent advancements in nanotechnology have driven the development of innovative materials and technologies for oil-contaminated wastewater treatment. Nanomaterials can offer enhanced oil-water separation properties due to their high surface area and tunable surface chemistry. The fabrication of nanofiber membranes with precise pore sizes and surface properties can further improve separation efficiency. Notably, novel technologies have emerged utilizing nanomaterials with special surface wetting properties, such as superhydrophobicity, to selectively separate oil from oil-water mixtures or emulsions. These special wetting surfaces are promising for high-efficiency oil separation in emulsions and allow the use of materials with relatively large pores, enhancing throughput and separation efficiency. In this study, we introduce a facile and scalable method for fabrication of superhydrophobic-superoleophilic felt fabrics for oil/water mixture and emulsion separation. AlN nanopowders are hydrolyzed to create the desired microstructures, which firmly adhere to the fabric surface without the need for a binder resin, enabling specialized wetting properties. This approach is applicable regardless of the material's size and shape, enabling efficient separation of oil and water from oil-water mixtures and emulsions. The oil-water separation materials proposed in this study exhibit low cost, high scalability, and efficiency, demonstrating their potential for broad industrial applications.

• Genomics & Informatics
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• 제17권4호
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of Plant Biotechnology
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• 제37권2호
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• pp.115-124
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• 2010

As the completion of genome sequencing, large collection of expression data and the great efforts in annotating plant genomes, the next challenge is to systematically assign functions to all predicted genes in the genome. Functional genome analysis of plants has entered the high-throughput stage. The generations and collections of mutants at the genome-wide level form technological platform of functional genomics. However, to identify the exact function of unknown genes it is necessary to understand each gene's role in the complex orchestration of all gene activities in the plant cell. Gene function analysis therefore necessitates the analysis of temporal and spatial gene expression patterns. The most conclusive information about changes in gene expression levels can be gained from analysis of the varying qualitative and quantitative changes of messenger RNAs, proteins and metabolites. New technologies have been developed to allow fast and highly parallel measurements of these constituents of the cell that make up gene activity. We have reviewed currently employed technologies to identify unknown functions of predicted genes including map-based cloning, insertional mutagenesis, reverse genetics, chemical mutagenesis, microarray analysis, FOX-hunting system, gene silencing mutagenesis, proteomics and chemical genomics. Recent improvements in technologies for functional genomics enable whole-genome functional analysis, and thus open new avenues for studies of the regulations and functions of unknown genes in plants.