• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.808-810
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• 2003

본 논문에서는 생물학 관련 문서에서 단백질 간의 상호작용을 추출하는 방법에 대한 전반적인 기술 동향을 소개하고, 현재 구현된 상호작용 정보 자동추출 시스템의 연구 결과에 대해 기술한다. 일반적으로 이미 알려진 단백질들의 관계를 추출함에 있어서는 단백질의 이름에 대한 특성 구분과 표현의 의미적 해석등에 NLP 기법을 사용하여, 사용자 정의에 따른 룰을 생성하는 방법과 데이터 마이닝 기법을 적용하여, 단백질간의 관계를 자동적으로 추출하는 방법, 또한 위의 이 두가지 방법을 병행하는 방법이 현재 연구되고 있다. 이 논문에서는 자연언어처리 기법과 머신러닝 기법(SVM)을 이용하여, 단백질간의 상호작용에 관한 일반 생물 정보 문헌에서 추출하고, 그 성능을 테스트 해 보겠다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.256-258
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• 2004

단백질간의 상호작용에 대한 연구는 생물학적 프로세스를 이해하기 위해 중요한 부분이다. 이러한 단백질간의 상호작용에 대한 정보는 주로 생명과학 관련 연구논문에 존재하지만 컴퓨터로 자동으로 처리하여 상호작용에 관안 정보를 추출할 수 있기 위해서는 텍스트 마이닝 기술이 적용되어야 한다 바이오 텍스트 마이닝에서 대두되고 있는 중요한 쟁점은 대용량의 연구논문에서 필요한 정보를 어떻게 효율적으로 정확하게 추출할 것인가에 대한 내용이다. 또한, 관심이 있는 단백질의 종류나 관련성을 표시하는 문장내 패턴의 다양성을 수용하기 위하여 개발하는 시스템의 확장성을 높이는 것도 소프트웨어 공학적인 측면에서 중요한 이슈이다 이 논문의 목적은 생물학적 내용을 담고 있는 연구논문으로부터 단백질간의 상호작용을 추출하는 확장성을 가진 텍스트 마이닝 기법을 제안하는데 있다.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.3-3
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• 2003

With biomedical literature expanding so rapidly, there is an urgent need to discover and organize knowledge extracted from texts. Although factual databases contain crucial information the overwhelming amount of new knowledge remains in textual form (e.g. MEDLINE). In addition, new terms are constantly coined as the relationships linking new genes, drugs, proteins etc. As the size of biomedical literature is expanding, more systems are applying a variety of methods to automate the process of knowledge acquisition and management. In my talk, I focus on the project, GENIA, of our group at the University of Tokyo, the objective of which is to construct an information extraction system of protein - protein interaction from abstracts of MEDLINE. The talk includes (1) Techniques we use fDr named entity recognition (1-a) SOHMM (Self-organized HMM) (1-b) Maximum Entropy Model (1-c) Lexicon-based Recognizer (2) Treatment of term variants and acronym finders (3) Event extraction using a full parser (4) Linguistic resources for text mining (GENIA corpus) (4-a) Semantic Tags (4-b) Structural Annotations (4-c) Co-reference tags (4-d) GENIA ontology I will also talk about possible extension of our work that links the findings of molecular biology with clinical findings, and claim that textual based or conceptual based biology would be a viable alternative to system biology that tends to emphasize the role of simulation models in bioinformatics.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.2
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• pp.309-336
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• 2016

This paper introduces a relation extraction system that can be used in identifying and classifying semantic relations between biomedical entities in scientific texts using machine learning methods such as Support Vector Machines (SVM). The suggested system includes many useful functions capable of extracting various linguistic features from sentences having a pair of biomedical entities and applying them into training relation extraction models for maximizing their performance. Three globally representative collections in biomedical domains were used in the experiments which demonstrate its superiority in various biomedical domains. As a result, it is most likely that the intensive experimental study conducted in this paper will provide meaningful foundations for research on bio-text analysis based on machine learning.

• Korean Journal of Acupuncture
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• v.37 no.2
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• pp.88-96
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• 2020

Objectives : The purpose of this study was to investigate the anti-oxidant and anti-aging effect of the seed of Euphorbia lathyris L. extracted by supercritical CO₂. Methods : Human dermal fibroblast cells dosed with the extract from Euphorbia lathyris L. were harvested and the intracellular proteome was analyzed to examine the expression of proteins related collagen synthesis pathway, metalloproteinases (MMPs), extracellular matrix (ECM)-cell interaction, cytokines, and antioxidant enzymes by 2-dimensional gel electrophoresis. Results : Fatty acid analysis of the extract from Euphorbia lathyris L. showed oleic acid was 84% and linoleic acid was 4.1%. Antioxidative effect was about 53% by beta carotene bleaching assay. In 2-dimensional polyacrylamide gel electrophoresis (2-D PAGE) analysis, fifteen protein changes in five mechanisms which were collagen synthesis pathway, MMPs, ECM-cell interaction, cytokines, and antioxidant enzymes were analyzed. Conclusions : This study suggests the supercritical extraction from the seed of Euphorbia lathyris L. could be used as anti-oxidant substances for pharmacopuncture.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.1
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• pp.21-25
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• 2002

The contribution of lipid to thermal flayer generation from glucose-protein reaction was accomplished by isolating flavor compounds from casein-glucose (CG)and casein-glucose-coin oil (CGL) which were stored for 2 and 4 weeks at 6$0^{\circ}C$ and then reacted at 16$0^{\circ}C$ for 1hr. The volatiles from the reactant mixtures were isolated by a solvent extraction method with methylene chloride and analyzed by gas chromatography and gas chromatography-mass spectrometry. Pyrazine, methylpyrazine, 2,5-dimethylpyrazine, 2-dimethylpyrazine ,2-ethy-5- methyIpyrazine and 2-acetylpyrrole originated from interaction of thermal degradation of casein and lipid oxidation were identified in the CGL samples. It was also found that 3-methyl-1-butanol, 2-cyclopene-1,4-diode, heptanal, nonanal, and 2-heptanone were derived from lipid source. Two additional fatty acids, heptanoic acid and octanoic acid were also identified in the CGL samples. 5-Hydroxymethyl-2-furfural, the most abundant volatile, was responsible for the formation of sugar degradation product. The results suggested that the presence of lipid in the samples had more effect on the contribution of volatile formation of glucose-protein thermal reaction than the absence of lipid in the samples.

• Journal of Microbiology and Biotechnology
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• v.24 no.12
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• pp.1690-1698
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• 2014

Partial purification of glucoamylase from solid-state fermentation culture was, firstly, investigated by reverse micellar extraction (RME). To avoid back extraction problems, the glucoamylase was kept in the original aqueous phase, while the other undesired proteins/enzymes were moved to the reverse micellar organic phase. The individual and interaction effects of main factors (i.e., pH and NaCl concentration in the aqueous phase, and concentration of sodium bis-2-ethyl-hexyl-sulfosuccinate (AOT) in the organic phase) were studied using response surface methodology. The optimum conditions for the maximum recovery of the enzyme were pH 2.75, 100 mM NaCl, and 200 mM AOT. Furthermore, the optimum organic to aqueous volume ratio ($V_{org}/V_{aq}$) and appropriate number of sequential extraction stages were 2 and 3, respectively. Finally, 60% of the undesired enzymes including proteases and xylanases were removed from the aqueous phase, while 140% of glucoamylase activity was recovered in the aqueous phase and the purification factor of glucoamylase was found to be 3.0-fold.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.64-73
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• 2004

세포 내에서 일어나는 신호 전달 과정은 단백질간의 상호작용을 통해 수행되고 조절된다. 단백질 상호작용 데이터를 활용하여 수행된 연구로는 단백질의 기능을 유추하거나 전체 네트워크 중 다른 지역보다 더 조밀한 상호작용을 추출하여 complex 혹은 pathway를 발견하고 진화 과정을 이해하는 바탕이 되고 있다. 본 연구에서는 신호 전달 경로에 대한 사전 정보 없이 yeast 상호작용 정보와 녹색형광단백질(GFP)을 이용하여 밝혀진 4000여 개의 yeast 단백질 위치 분포 data를 이용하여 신호전달경로를 찾는 방법을 시도했다. 기존 연구에 의해 밝혀진 yeast 내의 단백질 위치 분포 결과를 보면 21개의 category에 대해 각 단백질 상호작용 분포가 다양하게 나타나고, 특정 위치에서 상호작용 빈도수가 현저히 크다는 것을 알 수 있다. 특히 두 단백질이 같은 장소에 있을 경우 상호작용 확률이 높으며, 세포 내 소기관 사이에도 상호작용의 정도가 다양함이 알려져 있다. 따라서 이러한 분포상의 특성을 고려하여 상호작용을 기반으로 하여 세포막 단백질을 출발점으로, 핵에 있는 단백질을 도착점으로 잡고, 그 사이에 존재하는 다양한 가능 경로 중에서 단백질의 위치 정보를 가중치로 사용하여 그 중 최대 가능 경로를 찾도록 구현하였다. 이와 같은 pathway 모델링은 기존에 밝혀진 pathway와의 비교를 통해 알려지지 않은 새로운 경로를 발견하고, 이전에 경로에 참여하지 않은 단백질들을 발견할 수 있고, 이미 알려진 단백질들의 새로운 기능들에 대해서도 추론할 수 있을 것이라 기대한다.

• Molecular & Cellular Toxicology
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• v.5 no.3
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• pp.230-236
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• 2009

Acrolein, a known toxin in cigarette smoke, is the most abundant electrophilic $\alpha$, $\beta$-unsaturated aldehyde to which humans are exposed in a variety of environmental pollutants, and is also product of lipid peroxidation. Increased unsaturated aldehyde levels and reduced antioxidant status plays a major role in the pathogenesis of various diseases such as diabetes, Alzheimer's and atherosclerosis. The findings reported here show that low concentrations of acrolein induce heme oxygenase-1 (HO-1) expression in RAW 264.7 macrophages. HO-1 induction by acrolein and signal pathways was measured using reverse transcription-polymerase chain reaction, Western blot and immunofluorescence staining analyses. Inhibition of extracellular signal-regulated kinase activity significantly attenuated the induction of HO-1 protein by acrolein, while suppression of Jun N-terminal kinase and p38 activity did not affect induction of HO-1 expression. Moreover, rottlerin, an inhibitor of protein kinase $\delta$, suppressed the upregulation of HO-1 protein production, possibly involving the interaction of NF-E2-related factor 2 (Nrf2), which has a key role as a HO-1 transcription factor. Acrolein elevated the nuclear translocation of Nrf2 in nuclear extraction. The results suggest that RAW 264.7 may protect against acrolein-mediated cellular damage via the upregulation of HO-1, which is an adaptive response to oxidative stress.