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General rules for functional microRNA targeting
Kim, Doyeon,Sung, You Me,Park, Jinman,Kim, Sukjun,Kim, Jongkyu,Park, Junhee,Ha, Haeok,Bae, Jung Yoon,Kim, SoHui,Baek, Daehyun Nature Pub. Co 2016 Nature genetics Vol.48 No.12
<P>The functional rules for microRNA (miRNA) targeting remain controversial despite their biological importance because only a small fraction of distinct interactions, called site types, have been examined among an astronomical number of site types that can occur between miRNAs and their target mRNAs. To systematically discover functional site types and to evaluate the contradicting rules reported previously, we used large-scale transcriptome data and statistically examined whether each of approximately 2 billion site types is enriched in differentially downregulated mRNAs responding to overexpressed miRNAs. Accordingly, we identified seven non-canonical functional site types, most of which are novel, in addition to four canonical site types, while also removing numerous false positives reported by previous studies. Extensive experimental validation and significantly elevated 3' UTR sequence conservation indicate that these non-canonical site types may have biologically relevant roles. Our expanded catalog of functional site types suggests that the gene regulatory network controlled by miRNAs may be far more complex than currently understood.</P>
TALEN-based knockout library for human microRNAs
Kim, Young-Kook,Wee, Gabbine,Park, Joha,Kim, Jongkyu,Baek, Daehyun,Kim, Jin-Soo,Kim, V Narry Nature Publishing Group, a division of Macmillan P 2013 Nature structural & molecular biology Vol.20 No.12
Various technical tools have been developed to probe the functions of microRNAs (miRNAs), yet their application has been limited by low efficacy and specificity. To overcome the limitations, we used transcription activator–like effector nucleases (TALENs) to knock out human miRNA genes. We designed and produced a library of 540 pairs of TALENs for 274 miRNA loci, focusing on potentially important miRNAs. The knockout procedure takes only 2–4 weeks and can be applied to any cell type. As a case study, we generated knockout cells for two related miRNAs, miR-141 and miR-200c, which belong to the highly conserved miR-200 family. Interestingly, miR-141 and miR-200c, despite their overall similarity, suppress largely nonoverlapping groups of targets, thus suggesting that functional miRNA-target interaction requires strict seed-pairing. Our study illustrates the potency of TALEN technology and provides useful resources for miRNA research.
인공신경망을 이용한 저항 점용접 너겟 직경 예측에 관한 연구
김종규(Jongkyu Kim),구자훈(JaHun Ku),박영도(Yeongdo Park),김영창(Youngchang Kim),황영민(Youngmin Hwang),김희수(Heesoo Kim),Siva Prasad Murugan,구남국(Namkug Ku) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.6
Resistance spot welding, which has the advantages of low cost and high productivity, is the most common method used in the automobile industry for joining steel sheets. However, in practice, resistance spot welds are typically tested for welding quality using destructive rather than non-destructive inspection methods because of their lower cost. However, in destructive inspection, quality defects can be found only after the completion of the process. Accordingly, several studies are currently being conducted to predict the quality of welding in real time. Welding quality is determined by the diameter of the nugget, and its size depends on several independent variables. In this study, a linear regression model and artificial neural network model were constructed to predict the nugget diameter. An electric power pattern was obtained from the results of a welding experiment, and nine types of electric power characteristic values were extracted from the obtained electric power pattern as independent variables. From the nine electric power characteristic values, six having the highest correlation with the nugget diameter were determined as final independent variables through correlation analysis. The linear regression model was constructed using multiple linear regression analysis, and the artificial neural network model was built using a deep neural network model with two hidden layers and nodes of 64 and 16. In this study, the error between the actual measured and predicted nugget diameters was taken as 0.2 ㎜ or less as a good predictive value. When the linear regression model was used to predict the nugget diameter, only approximately 36% were predicted well. By contrast, when the artificial neural network was used, approximately 86% were predicted well. Thus, the artificial neural network model yielded better results. It was determined that with more welding data and information on steel types, the proposed welding quality prediction system could be improved.
Jongkyu Kim,박영도,구남국 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7
The automobile industry uses resistance spot welding, which is advantageous in terms of cost and productivity, for joining steel sheets the most. However, in actual field, the cost of inspection for quality evaluation is high. Therefore, research for real time prediction of the weld quality is ongoing. This study is focused on studying the button diameter prediction using artificial neural network and the power data monitored during the welding. The artificial neural network model was developed as a deep neural network model, the obtained predictions using the model are compared with the actual button diameter. As a result, a coefficient of determination of 0.99 and a root mean square error of 0.06 mm are obtained from the developed model.