• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.56-60
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• 2016

Peptide identification in tandem mass spectrometry is usually done by searching the spectra against target databases consisting of reference protein sequences. To control false discovery rates for high-confidence peptide identification, spectra are also searched against decoy databases constructed by permuting reference protein sequences. In this case, a peptide of the same sequence could be included in both the target and the decoy databases or multiple entries of a same peptide could exist in the decoy database. These phenomena make the protein identification problem complicated. Thus, it is important to minimize the number of such redundant peptides for accurate protein identification. In this regard, we examined two popular methods for decoy database generation: 'pseudo-shuffling' and 'pseudo-reversing'. We experimented with target databases of varying sizes and investigated the effect of the maximum number of missed cleavage sites allowed in a peptide (MC), which is one of the parameters for target and decoy database generation. In our experiments, the level of redundancy in decoy databases was proportional to the target database size and the value of MC, due to the increase in the number of short peptides (7 to 10 AA). Moreover, 'pseudo-reversing' always generated decoy databases with lower levels of redundancy compared to 'pseudo-shuffling'.

• Journal of Pharmacopuncture
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• v.18 no.3
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• pp.11-18
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• 2015

Objectives: Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era. Methods: The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction. Results: The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted. Conclusion: Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

• Herbal Formula Science
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• v.28 no.3
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• pp.243-254
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• 2020

Objectives : Network pharmacology analysis is commonly used to investigate the synergies and potential mechanisms of multiple compounds by analyzing complex, multi-layered networks. We used TCMSP and BATMAN-TCM databases to compare results of network pharmacological analysis between White Ginseng(WG) and Red Ginseng(RG). Methods : WG and RG were compared with components and their target molecules using TCMSP database, and compound-target-pathway/disease networks were compared using BATMAN-TCM database. Results : Through TCMSP, 104 kinds of target molecules were derived from WG and 38 kinds were derived from RG. Using the BATMAN-TCM database, target pathways and diseases were screened, and more target pathways and diseases were screened compared to RG due to the high composition of WG ingredients. Analysis of component-target-pathway/disease network using network analysis tools provided by BATMAN-TCM showed that WG formed more networks than RG. Conclusions : Network pharmacology analysis can be effectively performed using various databases used in system biology research, and although the materials that have been reported in the past can be used efficiently for research on diseases related to targets, the results are unreliable if prior studies are focused on limited or narrow research areas.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.372-380
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• 2003

Temporal databases support time-varying events so that conventional aggregate functions are extended to be processed with time for temporal aggregate functions. In the previous approach, it is done repeatedly to find time intervals and is calculated the result of each interval whenever target events are different. This paper proposes a method which processes temporal aggregate function queries using time point sequence. We can make time point sequence storing the start time and the end time of events in temporal databases in advance. It is also needed to update time point sequence due to insertion or deletion of events in temporal databases. Because time point sequence maintains the information of time intervals, it is more efficient than the previous approach when temporal aggregate function queries are continuously requested, which have different target events.

• Genomics & Informatics
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• v.17 no.1
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• pp.10.1-10.3
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• 2019

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.

• BMB Reports
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• v.55 no.9
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• pp.417-428
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• 2022

Herbal medicine, a multi-component treatment, has been extensively practiced for treating various symptoms and diseases. However, its molecular mechanism of action on the human body is unknown, which impedes the development and application of herbal medicine. To address this, recent studies are increasingly adopting systems pharmacology, which interprets pharmacological effects of drugs from consequences of the interaction networks that drugs might have. Most conventional network-based approaches collect associations of herb-compound, compound-target, and target-disease from individual databases, respectively, and construct an integrated network of herb-compound-target-disease to study the complex mechanisms underlying herbal treatment. More recently, rapid advances in high-throughput omics technology have led numerous studies to exploring gene expression profiles induced by herbal treatments to elicit information on direct associations between herbs and genes at the genome-wide scale. In this review, we summarize key databases and computational methods utilized in systems pharmacology for studying herbal medicine. We also highlight recent studies that identify modes of action or novel indications of herbal medicine by harnessing drug-induced transcriptome data.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.10a
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• pp.27-40
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• 2004

When we derive multi-scale databases from a source spatial database, thegeometries and topological relations in the source database are transformed according to a predefined set of constraints. This means that the derived databases should be checked to see if the constraints are respected during the construction or updates of databases and to maintain the consistency of multi-scale databases. In this paper, we focus on the topological consistency between the source and derived databases, which is one of the important constraints to respect. In particular, we deal with the method of assessment of topological consistency, when 2-dimensional objects are collapsed to 1-dimensional ones. We introduce eight types of topological relations between 2-dimensional objects and 19 topological ones between 1-dimensional objects and propose four different strategies to convert 2-dimensional topological relations in the source database to 1-dimensional ones objects in the target database. With these strategies, we guarantee the topological consistency between multi-scale databases.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.250-257
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• 2002

Clustering is a data mining method, which consists in discovering interesting data distributions in very large databases. In traditional data clustering, similarity of a cluster of object is measured by pairwise similarity of objects in that paper. In view of the nature of clustering transactions, we devise in this paper a novel measurement called item similarity and utilize this to perform clustering. With this item similarity measurement, we develop an efficient clustering algorithm for target marketing in each group.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.12-21
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• 2021

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.113-121
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• 2023

Network pharmacology in traditional Korean and Chinese medicine studies the molecular and biological aspects of herbal medicine using computational methods. Despite variations in databases, techniques, and criteria, most studies follow similar steps: constructing herb-compound networks, compound-target networks, and target interpretation. To ensure efficient and consistent analysis in herbal medicine network pharmacology, we designed and implemented a common analysis pipeline. We showed its reliability with existing databases. The proposed system has a potential to facilitate network pharmacology analysis in traditional medicine, ensuring consistent analysis of various herbal medicines.