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Kim, Yikwon,Kang, MeeJoo,Han, Dohyun,Kim, Hyunsoo,Lee, KyoungBun,Kim, Sun-Whe,Kim, Yongkang,Park, Taesung,Jang, Jin-Young,Kim, Youngsoo American Chemical Society 2016 JOURNAL OF PROTEOME RESEARCH Vol.15 No.1
<P>Intraductal papillary mucinous neoplasm (IPMN) is a common precursor of pancreatic cancer (PC). Much clinical attention has been directed toward IPMNs due to the increase in the prevalence of PC. The diagnosis of IPMN depends primarily on a radiological examination, but the diagnostic accuracy of this tool is not satisfactory, necessitating the development of accurate diagnostic biomarkers for IPMN to prevent PC. Recently, high-throughput targeted proteomic quantification methods have accelerated the discovery of biomarkers, rendering them powerful platforms for the evolution of IPMN diagnostic biomarkers. In this study, a robust multiple reaction monitoring (MRM) pipeline was applied to discovery and verify IPMN biomarker candidates in a large cohort of plasma samples. Through highly reproducible MRM assays and a stringent statistical analysis, 11 proteins were selected as IPMN marker candidates with high confidence in 184 plasma samples, comprising a training (n = 84) and test set (n = 100). To improve the discriminatory power, we constructed a six-protein panel by combining marker candidates. The multimarker panel had high discriminatory power in distinguishing between IPMN and controls, including other benign diseases. Consequently, the diagnostic accuracy of IPMN can be improved dramatically with this novel plasma-based panel in combination with a radiological examination.</P>
Hierarchical structural component modeling of microRNA-mRNA integration analysis
Kim, Yongkang,Lee, Sungyoung,Choi, Sungkyoung,Jang, Jin-Young,Park, Taesung BioMed Central 2018 BMC bioinformatics Vol.19 No.-
<P><B>Background</B></P><P>Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification.</P><P><B>Results</B></P><P>It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a <I>hi</I>erarchical <I>s</I>tructured <I>com</I>ponent analysis of <I>mi</I>croRNA-<I>m</I>RNA <I>i</I>ntegration (“HisCoM-mimi”) model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods.</P><P><B>Conclusion</B></P><P>As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.</P>
Temperature Dependence of Exchange Coupling on Magnetic Tunnel Junctions
Yongkang Hu,CheolGi Kim,Tomasz Stobiecki,Chong-Oh Kim,Kimin Hong 한국자기학회 2003 Journal of Magnetics Vol.8 No.1
Magnetic Tunnel Junctions (MTJs) were fabricated on thermally oxidized Si (100) wafers using DC magnetron sputtering. The film structures were Ta(50 Å)/Cu(100 Å)/Ta(50 Å)/Ni_(80)Fe_(20)(20 Å)/Cu(50 Å)/Mn_(75)Ir_(25)(100 Å)/Co_(70)Fe_(30)(25 Å)/Al-O(15 Å)/Co_(70)Fe_(30)(25 Å)/Ni_(80)Fe_(20)(t)/Ta(50 Å), with t = 0 Å, 100 and 1000 Å, respectively. X-ray diffraction has shown improvement of (111) texture of IrMn₃ and Cu by annealing. The exchange-biased energy is almost inversely proportional to temperature. The difference between the coercivity Hc and the exchange biased field H_E for t = 0 Å sample is smaller than that for t = 1000 Å. For the pinned layer, the decreasing rate of the coercivity with the temperature is higher compared to that of the exchange field, but variation of Hc is similar to that of the exchange field for free layer.
Kim, Yongkang,Park, Taesung Korea Genome Organization 2019 Genomics & informatics Vol.17 No.1
To identify miRNA-mRNA interaction pairs associated with binary phenotypes, we propose a hierarchical structural component model for miRNA-mRNA integration (HisCoM-mimi). Information on known mRNA targets provided by TargetScan is used to perform HisCoM-mimi. However, multiple databases can be used to find miRNA-mRNA signatures with known biological information through different algorithms. To take these additional databases into account, we present our advanced application software for HisCoM-mimi for binary phenotypes. The proposed HisCoM-mimi supports both TargetScan and miRTarBase, which provides manually-verified information initially gathered by text-mining the literature. By integrating information from miRTarBase into HisCoM-mimi, a broad range of target information derived from the research literature can be analyzed. Another improvement of the new HisCoM-mimi approach is the inclusion of updated algorithms to provide the lasso and elastic-net penalties for users who want to fit a model with a smaller number of selected miRNAs and mRNAs. We expect that our HisCoM-mimi software will make advanced methods accessible to researchers who want to identify miRNA-mRNA interaction pairs related with binary phenotypes.
Yun, Yeo-Min,Song, Junghan,Ji, Misuk,Kim, Jeong-Ho,Kim, Yongkang,Park, Taesung,Song, Sang Hoon,Park, Seungman,Kim, Min Jin,Nho, Sun Jin,Oh, Kyung Won The Korean Society for Laboratory Medicine 2017 Annals of Laboratory Medicine Vol.37 No.1
<P><B>Background</B></P><P>For correct interpretation of the high-density lipoprotein cholesterol (HDL-C) data from the Korea National Health and Nutrition Examination Survey (KNHANES), the values should be comparable to reference values. We aimed to suggest a way to calibrate KNHANES HDL-C data from 2008 to 2015 to the Centers for Disease Control and Prevention (CDC) reference method values.</P><P><B>Methods</B></P><P>We derived three calibration equations based on comparisons between the HDL-C values of the KNHANES laboratory and the CDC reference method values in 2009, 2012, and 2015 using commutable frozen serum samples. The selection of calibration equation for correcting KNHANES HDL-C in each year was determined by the accuracy-based external quality assurance results of the KNHANES laboratory.</P><P><B>Results</B></P><P>Significant positive biases of HDL-C values were observed in all years (2.85-9.40%). We created the following calibration equations: standard HDL-C=0.872×[original KNHANES HDL-C]+2.460 for 2008, 2009, and 2010; standard HDL-C=0.952×[original KNHANES HDL-C]+1.096 for 2012, 2013, and 2014; and standard HDL-C=1.01×[original KNHANES HDL-C]-3.172 for 2011 and 2015. We calibrated the biases of KNHANES HDL-C data using the calibration equations.</P><P><B>Conclusions</B></P><P>Since the KNHANES HDL-C values (2008-2015) showed substantial positive biases compared with the CDC reference method values, we suggested using calibration equations to correct KNHANES data from these years. Since the necessity for correcting the biases depends on the characteristics of research topics, each researcher should determine whether to calibrate KNHANES HDL-C data or not for each study.</P>
Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes
Lee, Sungyoung,Kim, Yongkang,Choi, Sungkyoung,Hwang, Heungsun,Park, Taesung BioMed Central 2018 BMC bioinformatics Vol.19 No.-
<P><B>Background</B></P><P>As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways.</P><P><B>Results</B></P><P>Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset.</P><P><B>Conclusions</B></P><P>Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously.</P>