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Using Biomedical Knowledge Graph Reasoning for Drug Repurposing
Using Biomedical Knowledge Graph Reasoning for Drug Repurposing
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151452
- ISBN
- 9798383200520
- DDC
- 574
- 저자명
- Tu, Roger.
- 서명/저자
- Using Biomedical Knowledge Graph Reasoning for Drug Repurposing
- 발행사항
- [Sl] : The Scripps Research Institute, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 195 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: A.
- 주기사항
- Advisor: Su, Andrew I.
- 학위논문주기
- Thesis (Ph.D.)--The Scripps Research Institute, 2024.
- 초록/해제
- 요약Drug repurposing, identifying new uses for approved drugs, has emerged as an urgent and significant drug development approach due to the escalating costs associated with bringing a drug to market. Computational drug repurposing can leverage scalable in-silico methods like knowledge graphs to quickly model, screen, or predict drug repurposing indications (disease treatments). By manipulating massed interconnected descriptions of drug and disease entities and their underlying relationships in a biomedical knowledge graph, computational drug repurposing approaches can identify candidate indications. However, as knowledge graphs are incomplete, absent connections can prevent potential discovery of a drug repurposing indication. Furthermore, similarity-based drug repurposing lacks mechanistic insights to support a potential repurposing candidate. My thesis highlights three contributions to advance computational drug repurposing utilizing knowledge graph completion, that is the identification of missing links in a graph, to ascertain potential drug repurposing indications in a biomedical context. First, I created a mechanistic drug repurposing knowledge graph and applied a path traversal algorithm to prioritize repurposing candidates. Next, I leveraged the combined strength of embedding and path-based reasoning approaches to improve drug repurposing performance. Finally, I created a time-based drug repurposing framework to facilitate the construction and analysis of a more realistic drug repurposing algorithm evaluation platform.
- 일반주제명
- Bioinformatics
- 일반주제명
- Pharmacology
- 일반주제명
- Information science
- 키워드
- Drug repurposing
- 키워드
- Embeddings
- 키워드
- Graph completion
- 키워드
- Knowledge graph
- 키워드
- Ontology
- 기타저자
- The Scripps Research Institute Computational Biology/Bioinformatics
- 기본자료저록
- Dissertations Abstracts International. 86-01A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383200520
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aTu, Roger.
■24510▼aUsing Biomedical Knowledge Graph Reasoning for Drug Repurposing
■260 ▼a[Sl]▼bThe Scripps Research Institute▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a195 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: A.
■500 ▼aAdvisor: Su, Andrew I.
■5021 ▼aThesis (Ph.D.)--The Scripps Research Institute, 2024.
■520 ▼aDrug repurposing, identifying new uses for approved drugs, has emerged as an urgent and significant drug development approach due to the escalating costs associated with bringing a drug to market. Computational drug repurposing can leverage scalable in-silico methods like knowledge graphs to quickly model, screen, or predict drug repurposing indications (disease treatments). By manipulating massed interconnected descriptions of drug and disease entities and their underlying relationships in a biomedical knowledge graph, computational drug repurposing approaches can identify candidate indications. However, as knowledge graphs are incomplete, absent connections can prevent potential discovery of a drug repurposing indication. Furthermore, similarity-based drug repurposing lacks mechanistic insights to support a potential repurposing candidate. My thesis highlights three contributions to advance computational drug repurposing utilizing knowledge graph completion, that is the identification of missing links in a graph, to ascertain potential drug repurposing indications in a biomedical context. First, I created a mechanistic drug repurposing knowledge graph and applied a path traversal algorithm to prioritize repurposing candidates. Next, I leveraged the combined strength of embedding and path-based reasoning approaches to improve drug repurposing performance. Finally, I created a time-based drug repurposing framework to facilitate the construction and analysis of a more realistic drug repurposing algorithm evaluation platform.
■590 ▼aSchool code: 1179.
■650 4▼aBioinformatics
■650 4▼aPharmacology
■650 4▼aInformation science
■653 ▼aDrug repurposing
■653 ▼aEmbeddings
■653 ▼aGraph completion
■653 ▼aKnowledge graph
■653 ▼aOntology
■690 ▼a0715
■690 ▼a0723
■690 ▼a0419
■71020▼aThe Scripps Research Institute▼bComputational Biology/Bioinformatics.
■7730 ▼tDissertations Abstracts International▼g86-01A.
■790 ▼a1179
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161844▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
