• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.52-57
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• 2007

The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.

• Genomics & Informatics
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• v.13 no.3
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• pp.76-80
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• 2015

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

• Journal of Microbiology and Biotechnology
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• v.30 no.5
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• pp.633-641
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• 2020

Microbial rhodopsins are a superfamily of photoactive membrane proteins with the covalently bound retinal cofactor. Isomerization of the retinal chromophore upon absorption of a photon triggers conformational changes of the protein to function as ion pumps or sensors. After the discovery of proteorhodopsin in an uncultivated γ-proteobacterium, light-activated proton pumps have been widely detected among marine bacteria and, together with chlorophyll-based photosynthesis, are considered as an important axis responsible for primary production in the biosphere. Rhodopsins and related proteins show a high level of phylogenetic diversity; we focus on a specific class of bacterial rhodopsins containing the '3 omega motif.' This motif forms a stack of three non-consecutive aromatic amino acids that correlates with the B-C loop orientation and is shared among the phylogenetically close ion pumps such as the NDQ motif-containing sodium-pumping rhodopsin, the NTQ motif-containing chloride-pumping rhodopsin, and some proton-pumping rhodopsins including xanthorhodopsin. Here, we reviewed the recent research progress on these 'omega rhodopsins,' and speculated on their evolutionary origin of functional diversity.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.851-876
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• 2018

Dynamic time warping (DTW) is the main algorithms for time series alignment. However, it is unsuitable for quasi-periodic time series. In the current situation, except the recently published the shape exchange algorithm (SEA) method and its derivatives, no other technique is able to handle alignment of this type of very complex time series. In this work, we propose a novel algorithm that combines the advantages of the SEA and the DTW methods. Our main contribution consists in the elevation of the DTW power of alignment from the lowest level (Class A, non-periodic time series) to the highest level (Class C, multiple-periods time series containing different number of periods each), according to the recent classification of time series alignment methods proposed by Boucheham (Int J Mach Learn Cybern, vol. 4, no. 5, pp. 537-550, 2013). The new method (quasi-periodic dynamic time warping [QP-DTW]) was compared to both SEA and DTW methods on electrocardiogram (ECG) time series, selected from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) public database and from the PTB Diagnostic ECG Database. Results show that the proposed algorithm is more effective than DTW and SEA in terms of alignment accuracy on both qualitative and quantitative levels. Therefore, QP-DTW would potentially be more suitable for many applications related to time series (e.g., data mining, pattern recognition, search/retrieval, motif discovery, classification, etc.).

• Journal of Microbiology and Biotechnology
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• v.30 no.7
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• pp.1097-1103
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• 2020

Bacterial surface display systems have been developed for various applications in biotechnology and industry. Particularly, the discovery and design of anchoring motifs is highly important for the successful display of a target protein or peptide on the surface of bacteria. In this study, an efficient display system on Escherichia coli was developed using novel anchoring motifs designed from the E. coli mipA gene. Using the C-terminal fusion system of an industrial enzyme, Pseudomonas fluorescens lipase, six possible fusion sites, V¹⁴⁰, V¹⁷⁶, K¹⁷⁹, V²²⁶, V²³², and K²³⁴, which were truncated from the C-terminal end of the mipA gene (MV¹⁴⁰, MV¹⁷⁶, MV¹⁷⁹, MV²²⁶, MV²³², and MV²³⁴) were examined. The whole-cell lipase activities showed that MV¹⁴⁰ was the best among the six anchoring motifs. Furthermore, the lipase activity obtained using MV¹⁴⁰ as the anchoring motif was approximately 20-fold higher than that of the previous anchoring motifs FadL and OprF but slightly higher than that of YiaTR232. Western blotting and confocal microscopy further confirmed the localization of the fusion lipase displayed on the E. coli surface using the truncated MV¹⁴⁰. Additionally the MV¹⁴⁰ motif could be used for successfully displaying another industrial enzyme, α-amylase from Bacillus subtilis. These results showed that the fusion proteins using the MV¹⁴⁰ motif had notably high enzyme activities and did not exert any adverse effects on either cell growth or outer membrane integrity. Thus, this study shows that MipA can be used as a novel anchoring motif for more efficient bacterial surface display in the biotechnological and industrial fields.

• Proceedings of the KSRS Conference
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• v.2
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• IMMUNE NETWORK
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• v.10 no.3
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• pp.99-103
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• 2010

Background: Glycogen synthase kinase $3{\beta}$ ($GSK3{\beta}$) is a ubiquitous serine/threonine kinase that is regulated by serine phosphorylation at 9. Recent studies have reported the beneficial effects of a number of the pharmacological $GSK3{\beta}$ inhibitors in rodent models of septic shock. Since most of the $GSK3{\beta}$ inhibitors are targeted at the ATP-binding site, which is highly conserved among diverse protein kinases, the development of novel non-ATP competitive $GSK3{\beta}$ inhibitors is needed. Methods: Based on the unique phosphorylation motif of $GSK3{\beta}$, we designed and generated a novel class of $GSK3{\beta}$ inhibitor (GSK3i) peptides. In addition, we investigated the effects of a GSK3i peptide on lipopolysaccharide (LPS)-stimulated cytokine production and septic shock. Mice were intraperitoneally injected with GSK3i peptide and monitored over a 7-day period for survival. Results: We first demonstrate its effects on LPS-stimulated pro-inflammatory cytokine production including interleukin (IL)-6 and IL-12p40. LPS-induced IL-6 and IL-12p40 production in macrophages was suppressed when macrophages were treated with the GSKi peptide. Administration of the GSK3i peptide potently suppressed LPS-mediated endotoxin shock. Conclusion: Collectively, we present a rational strategy for the development of a therapeutic GSK3i peptide. This peptide may serve as a novel template for the design of non-ATP competitive GSK3 inhibitors.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.159-169
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• 1994

In this paper, we develop a prototype of ITS(Intelligent Tutoring Systems) system: UNIX TUTOR. It is designed for the purpose of teaching the UNIX beginners the principal concepts of UNIX and the shell commands using the communication between the student and the system. UNIX TUTOR engages the student in a two-way conversation that is mixed-initiative dialogue and attempts to teach the student UNIX via the Socratic method of guided discovery and the Coaching method interchangeably. And the student model is based on both the overlay model and the buggy model together. Thus TUTOR aims at teaching the students effectively whose levels of learning are different using various explanations which are determined by the student model. Because the knowledge representation for UNIX-TUTOR is based on the frame structure and the production rules it is easy to represent the complicated constructs. UNIX TUTOR is implemented on the SPARC station using X/Motif and C for cp command among 10 ones which were selected.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.1827-1830
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• 2002

단백질 모티프(motif)란 유사한 기능을 가진 여러 단백질 서열에서 공통적으로 발견되는 패턴으로서 단백질의 기능을 예측하는 단서로 활용된다. 현재 Prosite, Pfam 등의 데이터베이스에서 정규식(regular expression), 가중치 행렬(weighted matrix), 은닉 마코프 모델(hidden Markov model)의 형태로 4천여종 이상의 모티프가 등록되어 있다. 본 논문에서는 연관성 탐사 기법을 적용하여 Hits 데이터로부터 상당히 높은 연관성을 갖는 모티프 집단을 밝히고, 실제 자연현상에서 자주 나타나는 연관성을 교차타당성 (cross-validation) 기법을 통해 입증하였다. 이렇게 밝혀진 단백질 모티프간 연관성을 트라이 탐색 기법을 통해 웹으로 제공함으로써 단백질의 기능유추에 쉽게 접근하고자 한다.