• Journal of Gastric Cancer
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• v.13 no.3
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• pp.129-135
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• 2013

Gastric cancer is the second leading cause of cancer-related deaths worldwide. In advanced and metastatic gastric cancer, the conventional chemotherapy with limited efficacy shows an overall survival period of about 10 months. Patient specific and effective treatments known as personalized cancer therapy is of significant importance. Advances in high-throughput technologies such as microarray and next generation sequencing for genes, protein expression profiles and oncogenic signaling pathways have reinforced the discovery of treatment targets and personalized treatments. However, there are numerous challenges from cancer target discoveries to practical clinical benefits. Although there is a flood of biomarkers and target agents, only a minority of patients are tested and treated accordingly. Numerous molecular target agents have been under investigation for gastric cancer. Currently, targets for gastric cancer include the epidermal growth factor receptor family, mesenchymal-epithelial transition factor axis, and the phosphatidylinositol 3-kinase-AKT-mammalian target of rapamycin pathways. Deeper insights of molecular characteristics for gastric cancer has enabled the molecular classification of gastric cancer, the diagnosis of gastric cancer, the prediction of prognosis, the recognition of gastric cancer driver genes, and the discovery of potential therapeutic targets. Not only have we deeper insights for the molecular diversity of gastric cancer, but we have also prospected both affirmative potentials and hurdles to molecular diagnostics. New paradigm of transdisciplinary team science, which is composed of innovative explorations and clinical investigations of oncologists, geneticists, pathologists, biologists, and bio-informaticians, is mandatory to recognize personalized target therapy.

• Genomics & Informatics
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• v.14 no.1
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• pp.34-40
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• 2016

Due to advances in omics technologies, numerous genome-wide studies on human samples have been published, and most of the omics data with the associated clinical information are available in public repositories, such as Gene Expression Omnibus and ArrayExpress. While analyzing several public datasets, we observed that errors in gender information occur quite often in public datasets. When we analyzed the gender description and the methylation patterns of gender-specific probes (glucose-6-phosphate dehydrogenase [G6PD], ephrin-B1 [EFNB1], and testis specific protein, Y-linked 2 [TSPY2]) in 5,611 samples produced using Infinium 450K HumanMethylation arrays, we found that 19 samples from 7 datasets were erroneously described. We also analyzed 1,819 samples produced using the Affymetrix U133Plus2 array using several gender-specific genes (X (inactive)-specific transcript [XIST], eukaryotic translation initiation factor 1A, Y-linked [EIF1AY], and DEAD [Asp-Glu-Ala-Asp] box polypeptide 3, Y-linked [DDDX3Y]) and found that 40 samples from 3 datasets were erroneously described. We suggest that the users of public datasets should not expect that the data are error-free and, whenever possible, that they should check the consistency of the data.

• Molecules and Cells
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• v.38 no.4
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• pp.362-372
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• 2015

Setdb1, an H3-K9 specific histone methyltransferase, is associated with transcriptional silencing of euchromatic genes through chromatin modification. Functions of Setdb1 during development have been extensively studied in embryonic and mesenchymal stem cells as well as neurogenic progenitor cells. But the role of Sedtdb1 in myogenic differentiation remains unknown. In this study, we report that Setdb1 is required for myogenic potential of C2C12 myoblast cells through maintaining the expressions of MyoD and muscle-specific genes. We find that reduced Setdb1 expression in C2C12 myoblast cells severely delayed differentiation of C2C12 myoblast cells, whereas exogenous Setdb1 expression had little effect on. Gene expression profiling analysis using oligonucleotide microarray and RNA-Seq technologies demonstrated that depletion of Setdb1 results in downregulation of MyoD as well as the components of muscle fiber in proliferating C2C12 cells. In addition, exogenous expression of MyoD reversed transcriptional repression of MyoD promoter-driven luciferase reporter by Setdb1 shRNA and rescued myogenic differentiation of C2C12 myoblast cells depleted of endogenous Setdb1. Taken together, these results provide new insights into how levels of key myogenic regulators are maintained prior to induction of differentiation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.

• Genomics & Informatics
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• v.10 no.1
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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.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.667-684
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• 2022

Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying difficulties with current gene selection methods as well as future research directions are discussed.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.3
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• pp.158-166
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• 2023

Background: Copy number variation (CNV) can be identified using next-generation sequencing and microarray technologies, the research on the analysis of its association with meat traits in livestock breeding has significantly increased in recent years. Hanwoo is an inherent species raised in the Republic of Korea. It is now considered one of the most economically important species and a major food source mainly used for meat (Hanwoo beef). Methods: In this study, CNVs and the relationship between the obtained CNV regions (CNVRs) can be identified in the Hanwoo steer samples (n = 473) using Illumina Hanwoo SNP 50K bead chip and bioinformatic tools, which were used to locate the required data and meat traits were investigated. The PennCNV software was used for the identification of CNVs, followed by the use of the CNV Ruler software for locating the different CNVRs. Furthermore, bioinformatics analysis was performed. Results: We found a total of 2,575 autosomal CNVs (933 losses, 1,642 gains) and 416 CNVRs (289 gains, 111 losses, and 16 mixed), which were established with ranged in size from 2,183 bp to 983,333 bp and 10,004 bp to 381,836 bp, respectively. Upon analyzing the restriction of minor alleles frequency > 0.05 for meat traits association, 6 CNVRs in the carcass weight, 2 CNVRs in the marbling score, 3 CNVRs in the backfat thickness, and 2 CNVRs in the longissimus muscle area were related to the meat traits. In addition, we identified an overlap of 347 CNVRs. Moreover, 3 CNVRs were determined to have a gene that affects meat quality. Conclusions: Our results confirmed the relationship between Hanwoo CNVR and meat traits, and the possibility of overlapping candidate genes, annotations, and quantitative trait loci that results depended on to contribute to the greater understanding of CNVs in Hanwoo and its role in genetic variation among cattle livestock.