• Genomics & Informatics
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• v.17 no.3
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• pp.26.1-26.12
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• 2019

Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.11
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• pp.833-839
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• 2002

As the demand for slim laptops requires low-height optical disc drives, vibration problems of optical disc drives are of great concern. Additionally, with the decrease of a track width and a depth of focus in high density drives, studies on vibration resonance between mechanical parts become more important. From the vibration point of view, the performance of optical disc drives is closely related with the relative displacement between a disc and an objective lens which is controlled by servo mechanism. In other words, to read and write data properly, the relative displacement between an optical disc and an objective lens should be within a certain limit. The relative displacement is dependent on not only an anti-vibration mechanism design but also servo control capability. Good servo controls can make compensation for poor mechanisms, and vice versa. In a usual development process, robustness of the anti-vibration mechanism is always verified with the servo control of an objective lens. Engineers partially modify servo gain margin in case of a data reading error. This modification cannot correct the data reading error occasionally and the mechanism should be redesigned more robustly. Therefore it is necessary to verify a mechanism with respect to the possible servo gain plot. In this study we propose the experimental verification method for anti-vibration mechanism with respect to the existing servo gain plot. Thismethod verifies axial vibration characteristics of optical disc drives on the basis of transmissibility. Using this method, we verified our mechanism and modified the mechanism for better anti-vibration characteristics.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.