DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Doheon | - |
dc.contributor.advisor | 이도헌 | - |
dc.contributor.author | Yu, Hasun | - |
dc.contributor.author | 류하선 | - |
dc.date.accessioned | 2018-05-23T19:33:59Z | - |
dc.date.available | 2018-05-23T19:33:59Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675702&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/241803 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2017.2,[v, 84 p. :] | - |
dc.description.abstract | In silico network-based methods have been developed for reducing costs of drug development. Biological networks (bio-networks) consist of biological associations and are heterogeneous depending on different biological contexts. Here, we use context-based bio-networks for predicting effects of drugs in the human body. We predict drugs having opposite effects on disease genes as treatments of the diseases with ‘disease context’ and ‘effect type’ information. Also, we reconstruct an anatomical context-specific network including ‘multi-level entities’ such as genes, biological processes, and diseases. Our constructed network includes intercellular associations. We employ the network for analyzing effects of drugs on diseases. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | bio-network | - |
dc.subject | drug effects | - |
dc.subject | context | - |
dc.subject | effect type | - |
dc.subject | multi-level entities | - |
dc.subject | 바이오 네트워크 | - |
dc.subject | 약물 효능 예측 | - |
dc.subject | 상황정보 | - |
dc.subject | 관계 유형정보 | - |
dc.subject | 다 수준 엔티티 | - |
dc.title | Context-based directional bio-network analysis for drug effect prediction | - |
dc.title.alternative | 약물 효능 예측을 위한 상황정보 기반 바이오 네트워크 분석 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
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