DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이도헌 | - |
dc.contributor.author | Park, Jiseong | - |
dc.contributor.author | 박지성 | - |
dc.date.accessioned | 2024-07-25T19:30:59Z | - |
dc.date.available | 2024-07-25T19:30:59Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045791&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320603 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2023.8,[iv, 37 p. :] | - |
dc.description.abstract | De novo drug development are often costly, time-consuming and risky. Drug repurposing/repositioning, increasing usability of approved drugs, offers relatively high chance of success and efficiency based on the verified safety. Applying heterogeneous biological knowledge graph enabled compelling in silico drug repurposing, yet challenging to extract interaction between heterogeneous characteristics and utilizing multi-hop interactions. In this paper, I propose a PREDR model that predicts the drug-disease relationship by defining a drug-gene-disease meta-path from heterogeneous knowledge graphs and combining each heterogeneous relationship. The PREDR model reconstructs a meta-pathway based on relational information extracted by embedding each biological feature. The PREDR model outperformed compared to existing drug repurposing models in predicting drug-disease interaction, and demonstrated the effectiveness of meta-path reconstruction by showing higher performance than the result of learning and combining each heterogeneous relationships separately. The PREDR model can also explain the reaction mechanism of suggested drugs on the defined meta-pathway using heterogeneous interactions driven from reconstruction process. In predicting drug candidates for lymphoblastic leukemia disease conducted as a case study, the highest scored candidate is confirmed effectiveness of the disease in the literature, and predicted genes are verified to be targeted by both candidate and disease by various academic sources. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 약물 재창출▼a지식 그래프▼a메타경로▼a특성정보 임베딩 기반 예측 | - |
dc.subject | Drug repurposing▼aKnowledge graph▼aMeta-path▼aFeature vectors embedding based prediction | - |
dc.title | Drug repurposing through meta-path reconstruction using relationship embedding | - |
dc.title.alternative | 관계정보 임베딩 기반 메타경로 재구축을 통한 약물 재창출 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | Lee, Doheon | - |
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