Machine-learning based text-mining framework for extracting relationships between biomedical entities = 생물의학적 요소간 관계정보 추출을 위한 기계학습기반 텍스트마이닝 프레임워크

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To design new preventive and therapeutic strategies for diseases, it is necessary to understand relationships between diseases and drugs in a variety of perspectives such as the clinical-centric and the molecular-centric approach. In such approaches, the information about clinical factors and gene expressions are used for intermediates that link diseases and drugs. In this regard, it is important to collect the accurate and specific information about relationships between biomedical entities (disease, clinical factor, gene expression, and drug) in order to precisely interpret disease-drug relationships. Most of this knowledge is available via the biomedicalliterature. In this thesis, we introduce machine-learning based text-mining frameworks to extract relationships between biomedical entities from the scientific publications.
Advisors
Lee, Doheonresearcher이도헌researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2017.2,[iv, 97 p. :]

Keywords

Machine-learning; Text-mining; Information extraction; Bioinformatics; deep learning; 기계학습; 텍스트마이닝; 정보추출; 생명정보학; 딥러닝

URI
http://hdl.handle.net/10203/241806
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675698&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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