• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• Proceedings of the Materials Research Society of Korea Conference
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• 2010.05a
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• pp.45.2-45.2
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• 2010

Possessing of carbon nanotubes in biopolymer intrigued much interest due to their mechanical and unique nanoscale surface properties. Surface stiffness can be controlled by the amount of carbon nanotubes in polymer and surface wettability can be altered by the order of nanoscale surface roughness. Protein adsorption mechanism on nanostructured carbon nanotube/polymer thin film will be discussed in this study. In addition, we identified that mechanical stimuli also contribute the messenchymal stem cell and bone cell interactions. Importantly, live cell analysis system also showed altered morphology and cellular functions. Thus, embedding of carbon nanostructures simultaneously contribute to protein adsorption and cellular interactions. In conclusion, this study demonstrated the evidence that nanoscale surface features determine the subsequent biological interactions, such as protein adsorption and cellular interactions.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.194-198
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• 2017

In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

• Applied Microscopy
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• v.19 no.2
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• pp.74-84
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• 1989

Buffer soluble storage proteins of ginseng seed have been localized by electron microscopy using post-embedding immunocytochemical gold labelling technique. Major components of the storage proteins were revealed to be storage protein-1($SP_{1}$, MW 160,000) and storage protein-2($SP_{2}$, MW 70,000). Both of the storage proteins are glycoproteins. Anti-$SP_{1}$ and anti-$SP_{2}$ from rabbit, against $SP_1$ and $SP_2$, respectively, reacted on sections of ginseng endosperm tissue embedded in Spurr's epoxy resin. The rabbit antibodies were visualized indirectly by reaction with protein A labelled with colloidal gold. Both storage proteins were found to be accumulated together in the same protein bodies, but their relative contents are not equal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.321-323
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• 2016

최근 연구에서 기계학습 중 지도학습 방법으로 개체명 인식을 하고 있다. 그러나 지도 학습 방법은 데이터를 만드는 비용과 시간이 많이 필요로 한다. 본 연구에서는 주석 된 말뭉치를 사용하여 지도 학습 방법을 사용 한다. 의생명 개체명 인식은 Protein, RNA, DNA, Cell type, Cell line 등을 포함한 텍스트 처리에 중요한 기초 작업입니다. 그리고 의생명 지식 검색에서 가장 기본과 핵심 작업 중 하나이다. 본 연구에서는 순환형 신경망과 워드 임베딩을 자질로 사용한 조건적 임의 필드에 대한 성능을 비교한다. 조건적 임의 필드에 N_Gram만을 자질로 사용한 것을 기준점으로 설정 하였고, 기준점의 결과는 70.09% F1 Score이다. RNN의 jordan type은 60.75% F1 Score, elman type은 58.80% F1 Score의 성능을 보여준다. 조건적 임의 필드에 CCA, GLOVE, WORD2VEC을 사용 한 결과는 각각 72.73% F1 Score, 72.74% F1 Score, 72.82% F1 Score의 성능을 얻을 수 있다.

• Journal of Pharmacopuncture
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• v.18 no.4
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• pp.20-25
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• 2015

Objectives: Recently, thread-embedding therapy (TET) has been widely applied in Korean medicine for cosmetic purposes such as reducing skin wrinkles. An inserted thread was reported to have induced continuous stimulation, followed by support for connective tissue regeneration. However, the potential role of TET in hair-growth has not yet been reported. Methods: We designed this study to evaluate whether TET has a hair-growth-promoting effect. C57 black 6 (C57BL/6) mice were divided into three groups: normal saline-treated, minoxidil-treated, and thread-embedded groups. Normal saline or 5% minoxidil was topically sprayed on the dorsal skin of the mice once a day for 16 days. Medical threads were embedded into the dorsal skin of the mice in a single application. Hair growth activity was evaluated by using dermoscopic and microscopic observations. Sections of the dorsal skin were stained with hematoxylin and eosin. Expressions of bromodeoxyuridine (BrdU), proliferating cell nuclear antigen (PCNA), fibroblast growth factor-7 (FGF-7), and fibroblast growth factor-5 (FGF-5) were detected by using immunohistochemical staining. A reverse transcription-polymerase chain reaction (RT-PCR) analysis was adopted to measure the messenger RNA (mRNA) expressions of FGF-7 and FGF-5. Results: TET enhanced anagen development in the hair follicles of C57BL/6 mice. The expressions of BrdU and PCNA, both of which imply active cellular proliferation, were increased by using TET. Moreover, TET increased the expression of FGF-7, an anagen-inducing growth factor, while decreasing the expression of FGF-5, an anagen-cessation growth factor, both at the protein and the mRNA levels. Conclusion: TET enhanced hair re-growth in C57BL/6 mice. TET regulated the expressions of anagen-associated growth factors and activated the proliferation of hair follicular cells in depilated skin lesions. Considering its long-lasting effect, TET may be a good alternative therapeutic for the treatment of alopecia.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 1995.06b
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• pp.27-53
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• 1995

In plant pathology there is an increasing necessity for improved cytological techniques as basis for the localization of cellular substances within the dynamic fine structure of the host-(plant)-pathogen-interaction. Low temperature (LT) preparation techniques (shock freezing, freeze substitution, LT embedding) are now successfully applied in plant pathology. They are regarded as important tools to stabilize the dynamic plant-pathogen-interaction as it exists under physiological conditions. - The main advantage of LT techniques versus conventional chemical fixation is seen in the maintenance of the hydration shell of molecules and macromolecular structures. This results in an improved fine structural preservation and in a superior retention of the antigenicity of proteins. - A well defined ultrastructure of small, fungal organisms and large biological samples such as plant material and as well as the plant-pathogen (fungus) infection sites are presented. The mesophyll tissue of Arabidopsis thaliana is characterized by homogeneously structured cytoplasm closely attached to the cell wall. From analyses of the compatible interaction between Erysiphe graminis f. sp. hordei on barley (Hordeum vulgare), various steps in the infection sequence can be identified. Infection sites of powdery mildew on primary leaves of barley are analysed with regard to the fine structural preservation of the haustoria. The presentation s focussed on the ultrastructure of the extrahaustorial matrix and the extrahaustorial membrane. - The integration of improved cellular preservation with a molecular analysis of the infected host cell is achieved by the application of secondary probing techniques, i.e. immunocytochemistry. Recent data on the characterization of freeze substituted powdery mildew and urst infected plant tissue by immunogold methodology are described with special emphasis on the localization of THRGP-like (threonine-hydrxyproline-rich glycoprotein) epitopes. Infection sites of powdery mildew on barley, stem rust as well as leaf rust (Puccinia recondita) on primary leaves of wheat were probed with a polyclonal antiserum to maize THRGP. Cross-reactivity with the anti-THRGP antiserum was observed over the extrahaustorial matrix of the both compatible and incompatible plant-pathogen interactions. The highly localized accumulation of THRGP-like epitopes at the extrahaustorial host-pathogen interface suggests the involvement of structural, interfacial proteins during the infection of monocotyledonous plants by obligate, biotrophic fungi.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.321-323
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• 2016

최근 연구에서 기계학습 중 지도학습 방법으로 개체명 인식을 하고 있다. 그러나 지도 학습 방법은 데이터를 만드는 비용과 시간이 많이 필요로 한다. 본 연구에서는 주석 된 말뭉치를 사용하여 지도 학습 방법을 사용 한다. 의생명 개체명 인식은 Protein, RNA, DNA, Cell type, Cell line 등을 포함한 텍스트 처리에 중요한 기초 작업입니다. 그리고 의생명 지식 검색에서 가장 기본과 핵심 작업 중 하나이다. 본 연구에서는 순환형 신경망과 워드 임베딩을 자질로 사용한 조건적 임의 필드에 대한 성능을 비교한다. 조건적 임의 필드에 N_Gram만을 자질로 사용한 것을 기준점으로 설정 하였고, 기준점의 결과는 70.09% F1 Score이다. RNN의 jordan type은 60.75% F1 Score, elman type은 58.80% F1 Score의 성능을 보여준다. 조건적 임의 필드에 CCA, GLOVE, WORD2VEC을 사용 한 결과는 각각 72.73% F1 Score, 72.74% F1 Score, 72.82% F1 Score의 성능을 얻을 수 있다.

• BMB Reports
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• v.55 no.2
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• pp.72-80
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• 2022

Odorant receptors (ORs), the largest subfamily of G protein-coupled receptors, detect odorants in the nose. In addition, ORs were recently shown to be expressed in many nonolfactory tissues and cells, indicating that these receptors have physiological and pathophysiological roles beyond olfaction. Many ORs are expressed by tumor cells and tissues, suggesting that they may be associated with cancer progression or may be cancer biomarkers. This review describes OR expression in various types of cancer and the association of these receptors with various types of signaling mechanisms. In addition, the clinical relevance and significance of the levels of OR expression were evaluated. Namely, levels of OR expression in cancer were analyzed based on RNA-sequencing data reported in the Cancer Genome Atlas; OR expression patterns were visualized using t-distributed stochastic neighbor embedding (t-SNE); and the associations between patient survival and levels of OR expression were analyzed. These analyses of the relationships between patient survival and expression patterns obtained from an open mRNA database in cancer patients indicate that ORs may be cancer biomarkers and therapeutic targets.