Extracting herb-compound relations with quantity information from biomedical literature생의학 문헌으로부터 정량 정보를 포함한 약재-구성성분 관계 추출

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dc.contributor.advisorLee, Kwang Hyung-
dc.contributor.advisor이광형-
dc.contributor.authorJeong, Ryu Gwang-
dc.date.accessioned2018-06-20T06:17:26Z-
dc.date.available2018-06-20T06:17:26Z-
dc.date.issued2017-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=718596&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/242996-
dc.description학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2017.8,[iii, 45 p. :]-
dc.description.abstractTo develop the new drug and design strategies for diseases, it is required to understand the mechanism of herbal medicine. For this, it is important to construct networks of integrative relations among diseases, herbs, compounds and genes. Among these relations, it is necessary to understand the quantity information of compounds in herbs for high-resolution analysis about herbs have different effects to distinct phenotype. Most of this knowledge is available from the biomedical literature. In this study, we develop a machine-learning based text-mining model to extract herb-compound relations with quantity information from the biomedical literature.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectMachine-learning▼aInformation extraction▼aText-mining▼aBioinformatics▼aRelation extraction-
dc.subject기계학습▼a정보 추출▼a텍스트마이닝▼a바이오정보학▼a관계 추출-
dc.titleExtracting herb-compound relations with quantity information from biomedical literature-
dc.title.alternative생의학 문헌으로부터 정량 정보를 포함한 약재-구성성분 관계 추출-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :바이오및뇌공학과,-
dc.contributor.alternativeauthor정류광-
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BiS-Theses_Master(석사논문)
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