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Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms
Seo, Sangmin,Choi, Jonghwan,Ahn, Soon Kil,Kim, Kil Won,Kim, Jaekwang,Choi, Jaehyuck,Kim, Jinho,Ahn, Jaegyoon Hindawi 2018 Computational and mathematical methods in medicine Vol.2018 No.-
<P>We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.</P>
3D NAND Flash Memory의 극도로 좁은 V<SUB>th</SUB> 분포에 대한 Random Telegraph Noise 영향 조사
안상민(Sangmin Ahn),신형철(Hyungcheol Shin) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
We investigated the influence of random telegraph noise (RTN) on advanced multibit solution technology for achieving higher bit density in 3D NAND flash memory. Our simulations revealed that in Incremental Step Pulse Programming (ISPP) with low Vstep for achieving a narrow distribution, RTN has an even greater impact on the distribution. Thus, to achieve an extremely narrow distribution, it is necessary to reduce the magnitude of RTN.
Pre-service Teachers’ Learning Experience of Using a Virtual Practicum Simulation with AI Learners
( Sangmin Lee ),( Tae Youn Ahn ) 한국멀티미디어언어교육학회 2021 멀티미디어 언어교육 Vol.24 No.4
Practicum is essential for pre-service teachers to bridge the gap between theories and practice and prepare for the real classroom. However, it is often limited due to various reasons, such as time, cost, and a lack of partnership schools, in many countries. As an alternative to an actual practicum, the current paper explored a virtual practicum simulation, simSchool, to mitigate a shortage in reality-based preparation and investigated its effectiveness for pre-service teacher education. The study applied a mixed-method to perform more robust research. The study used quantitative content analysis of two versions of 37 pre-service teachers’ lesson plans (original and revised), reflection papers, and interviews as a primary method. As a secondary method, pre- and post-surveys on teacher efficacy were used. The results showed that pre-service teachers valued their experience with the virtual practicum and increased their abilities in diverse areas of teaching, including instruction, activities, facilitation, and material use. Their simSchool experience also helped the pre-service teachers realize their weaknesses in teaching and enabled them to transfer their newly learned knowledge to practice. Based on the results, the study argues that a virtual practicum offers pre-service teachers a valuable extended learning opportunity to develop their teaching skills, although it cannot replace real classroom teaching experience.
화학비료 및 돈분액비 사용에 따른 논에서의 비점배출 특성 분석
전상민 ( Sangmin Jun ),강문성 ( Moon Seong Kang ),송인홍 ( Inhong Song ),박지훈 ( Jihoon Park ),송정헌 ( Jung Hun Song ),김민지 ( Minji Kim ),안지현 ( Jihyun Ahn ) 한국농공학회 2014 한국농공학회 학술대회초록집 Vol.2014 No.-
비점오염원은 불특정 장소에서 불특정하게 수질오염물질을 배출하는 배출원을 의미한다. 비점오염원에는 농경지에 남아있는 비료와 농약, 초지에 방목된 가축의 배설물, 가축사육농가에서 배출되는 미처리 축산폐수, 빗물에 섞인 대기오염물질, 도로 노면의 퇴적물 등이 있다. 논 유출수에는 비료의 주요 성분인 질소와 인이 다량 포함되어 있어 농업 지역의 주요 비점오염원으로 여겨진다. 논에서 발생하는 비점오염이 하천에 미치는 영향을 분석하기 위해서는 사용되는 비료의 차이에 따른 비점오염 배출 특성을 분석할 필요가 있다. 따라서 본 연구에서는 화학비료 및 돈분액비를 사용하는 논을 각각 연구대상지로 선정하고, 수문 및 수질 모니터링을 통해 비료의 차이에 따른 논에서의 비점오염 배출 특성을 분석하고자 한다. 이를 위해 경기도 용인시 백암면 근삼리에 위치한 화학비료와 돈분액비를 각각 사용하는 논 2곳을 모니터링 대상지로 선정하였다. 각 논에는 초음파 수위계와 부자식 수위계가 부착된 위어를 설치하여 수위 및 배수량 자료를 수집하였다. 수질 모니터링으로 영농기 월 1회 이상 수질 샘플링을 실시하였고, 강우시에는 자동채수기를 이용해 정밀 모니터링을 실시하였다. 그리고 모니터링 결과를 바탕으로 화학비료 및 돈분액비 사용에 따른 논에서의 비점오염 배출 특성을 분석하였다.
서상민(Sangmin Seo),안재균(Jaegyoon Ahn) Korean Institute of Information Scientists and Eng 2019 정보과학회논문지 Vol.46 No.10
Characterizing the interactions between compounds and proteins is an important process for drug development and discovery. Structural data of proteins and compounds are used to identify their interactions, but those structural data are not always available, and the speed and accuracy of the predictions made in this way ware limited due to the large number of calculations involved. In this paper, compound-protein interactions were predicted using S2SAE (Sequence-To-Sequence Auto-Encoder), which is composed of a sequence-to-sequence algorithm used in machine translation as well as an auto-encoder for effective compression of the input vector. Compared to the existing method, the method proposed in this paper uses fewer features of protein-compound complex and also show higher predictive accuracy.