• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.157-176
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• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.

• BMB Reports
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• v.49 no.12
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• pp.671-680
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• 2016

Accumulating evidence indicates many brain functions are mediated by epigenetic regulation of neural genes, and their dysregulations result in neuronal disorders. Experiences such as learning and recall, as well as physical exercise, induce neuronal activation through epigenetic modifications and by changing the noncoding RNA profiles. Animal models, brain samples from patients, and the development of diverse analytical methods have broadened our understanding of epigenetic regulation in the brain. Diverse and specific epigenetic changes are suggested to correlate with neuronal development, learning and memory, aging and age-related neuronal diseases. Although the results show some discrepancies, a careful comparison of the data (including methods, regions and conditions examined) would clarify the problems confronted in understanding epigenetic regulation in the brain.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.418-424
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• 2005

The complexity of the biological system/biological systems has been fascinating and challenging for a long time. With the advent of mathematical tools with various omics technology, systems biology was born and is already ubiquitous in every area of biology and biotechnology. Microalgal biotechnology is no exception in this new trend. As tens of microalgal genomes become publicly available on the Internet, vast amounts of data from genomics, transcriptomics, and proteomics are reported everyday. Though there has not yet been enough data gathered on microalgal metabolomics, the in silica models for relatively simple cyanobacteria or for organelles, such as chloroplasts, will appear presently. With the help of systems biology, a more in-depth understanding of microalgae will be possible. Consequently, most industrially-interested microalgae can be metabolically redesigned/reconfigured as cell factories. Microalgae will be served as the hosts in white biotechnology.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.793-803
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• 2020

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

• Journal of the Korean Magnetic Resonance Society
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• v.21 no.3
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• pp.96-101
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• 2017

Metabolomics is one of latest '-omics', which is to analyze metabolome in cells, tissues and biofluids and to study metabolisms. It has become increasingly popular since 1990. The first goal of metabolomics is to analyze metabolites in a technical aspect. The major two analytical platforms in metabolomics are NMR spectroscopy and mass spectrometry (MS). MS is superior to NMR for detecting many more metabolites. That is one of the most important factors in metabolomics. However, NMR also has several advantages over MS. In this review, I firstly introduced metabolomics by comparing NMR-based metabolomics and MS-based metabolomics. Second, I explored technical issues on sample preparation and NMR experiments for metabolite identification and quantification.

• Genomics & Informatics
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• v.10 no.4
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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.

• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.3
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• pp.187-198
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• 2006

The aromatic hydrocarbons (AHs), which include benzene, polycyclic aromatic hydrocarbons, and dioxin, are important chemical and environmental contaminants in industry that usually cause various diseases. Over the years, numerous studies have described and evaluated the adverse health effects induced by AHs. Currently, "Omics" technologies, transcriptomics and proteomics, have been applied in AH toxicity studies. Proteomics has been used to identify molecular mechanisms and biomarkers associated with global chemical toxicity. It could enhance our ability to characterize chemical-induced toxicities and to identify noninvasive biomarkers. The proteomic approach (e.g. 2-dimensional electrophoresis [2-DE]), can be used to observe changes in protein expression during chemical exposure with high sensitivity and specificity. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and electrospray ionization-quadrupole (ESI-Q)-TOF MS/MS are recognized as the most important protein identification tools. This review describes proteomic technologies and their application in the proteomic analysis of AH toxicity.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.307-319
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• 2018

High-throughput technologies enable the simultaneous evaluation of thousands of genes that could discriminate different subclasses of complex diseases. Ranking genes according to differential expression is an important screening step for follow-up analysis. Many statistical measures have been proposed for this purpose. A good ranked list should provide a stable rank (at least for top-ranked gene), and the top ranked genes should have a high power in differentiating different disease status. However, there is a lack of emphasis in the literature on ranking genes based on these two criteria simultaneously. To achieve the above two criteria simultaneously, we proposed to apply a previously reported metric, the modified area under the receiver operating characteristic cure, to gene ranking. The proposed ranking method is found to be promising in leading to a stable ranking list and good prediction performances of top ranked genes. The findings are illustrated through studies on both synthesized data and real microarray gene expression data. The proposed method is recommended for ranking genes or other biomarkers for high-dimensional omics studies.

• Molecules and Cells
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• v.38 no.4
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• pp.297-303
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• 2015

Skeletal muscle insulin resistance, which increases the risk for developing various metabolic diseases, including type 2 diabetes, is a common metabolic disorder in obesity and aging. If potential treatments are to be developed to treat insulin resistance, then it is important to fully understand insulin signaling and glucose metabolism. While recent large-scale "omics" studies have revealed the acetylome to be comparable in size to the phosphorylome, the acetylation of insulin signaling proteins and its functional relevance to insulin-stimulated glucose transport and glucose metabolism is not fully understood. In this Mini Review we discuss the acetylation status of proteins involved in the insulin signaling pathway and review their potential effect on, and relevance to, insulin action in skeletal muscle.

• Journal of Preventive Medicine and Public Health
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• v.52 no.5
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• pp.277-280
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• 2019

The Ministry of Food and Drug Safety of Korea made an official announcement in March 2018 that the total number of inoculations of Hantaan virus vaccine ($Hantavax^{(R)}$) would change from 3 to 4. Some aspects of this decision remain controversial. Based on the characteristics of Hantaan virus (HTNV) and its role in the pathogenesis of hemorrhagic fever with renal syndrome, it might be difficult to develop an effective and safe HTNV vaccine through the isolate-inactivate-inject paradigm. With the development of high-through-put 'omics' technologies in the 21st century, vaccinomics has been introduced. While the goal of vaccinomics is to develop equations to describe and predict the immune response, it could also serve as a tool for developing new vaccine candidates and individualized approaches to vaccinology. Thus, the possibility of applying the innovative field of vaccinomics to develop a more effective and safer HTNV vaccine should be considered.