서브메뉴
검색
Generalizable Machine Learning Methods for Network Inference in Systems Biology
Generalizable Machine Learning Methods for Network Inference in Systems Biology
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151332
- ISBN
- 9798382814292
- DDC
- 574
- 서명/저자
- Generalizable Machine Learning Methods for Network Inference in Systems Biology
- 발행사항
- [Sl] : University of California, San Francisco, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 213 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Includes supplementary digital materials.
- 주기사항
- Advisor: Gartner, Zev.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2024.
- 초록/해제
- 요약Tissues comprise a multiplicity of specialized cell types that must coordinate state changes in order to function collectively. These state changes are orchestrated by coordinated direct cellular interactions and indirect responses to microenvironmental and systemic cues. Consequently, chronic perturbations to this collective behavior can result in disease states that are difficult to reprogram such as autoimmunity and cancer. As such, studying the self-reinforced dynamics of tissue function can benefit from a systems biology approach where the aim is to understand how individual components of biological systems interact to give rise to emergent properties.The recent growth in the availability of single-cell resolution genomics platforms has further expanded biologists' ability to do this kind of unbiased inquiry. However, despite the increasing ease of generating these high-dimensional datasets, analyzing these data still presents significant computational challenges because of their noise and sparsity, which are further exacerbated on the level of individual cells and genes. As such, there is a need to develop computational methods that enable scientists to extract systems-level biological insight from noisy high dimensional data.This dissertation introduces DECIPHER, a machine learning framework tailored for network inference in systems biology, with a focus on applications to single-cell RNA sequencing data. Chapter 2 details the DECIPHER algorithm and its implementation for the R computing environment, deciphR, that is designed to reconstruct cell state networks from high-dimensional molecular profiles. Chapter 3 applies DECIPHER to unveil cell-cell interaction networks in the human breast, elucidating how state changes on the cell-level propagate throughout tissue in response to hormonal fluctuations. Chapter 4 extends DECIPHER's application to investigate peripheral immune dysregulation in a rare pediatric autoimmune disease, revealing underlying immune imbalances that persist even in disease remission and potential therapeutic targets. Overall, this dissertation presents a generalizable approach to network inference for systems biology and demonstrates its utility in multiple biological contexts for unravelling cellular coordination in tissue homeostasis and disease.
- 일반주제명
- Bioinformatics
- 일반주제명
- Bioengineering
- 일반주제명
- Cellular biology
- 일반주제명
- Genetics
- 키워드
- Machine learning
- 키워드
- Systems biology
- 기타저자
- University of California, San Francisco Bioengineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017161270
■00520250211151332
■006m o d
■007cr#unu||||||||
■020 ▼a9798382814292
■035 ▼a(MiAaPQ)AAI31241334
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aRabadam, Gabrielle.▼0(orcid)0000-0001-8504-8983
■24510▼aGeneralizable Machine Learning Methods for Network Inference in Systems Biology
■260 ▼a[Sl]▼bUniversity of California, San Francisco▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a213 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aIncludes supplementary digital materials.
■500 ▼aAdvisor: Gartner, Zev.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2024.
■520 ▼aTissues comprise a multiplicity of specialized cell types that must coordinate state changes in order to function collectively. These state changes are orchestrated by coordinated direct cellular interactions and indirect responses to microenvironmental and systemic cues. Consequently, chronic perturbations to this collective behavior can result in disease states that are difficult to reprogram such as autoimmunity and cancer. As such, studying the self-reinforced dynamics of tissue function can benefit from a systems biology approach where the aim is to understand how individual components of biological systems interact to give rise to emergent properties.The recent growth in the availability of single-cell resolution genomics platforms has further expanded biologists' ability to do this kind of unbiased inquiry. However, despite the increasing ease of generating these high-dimensional datasets, analyzing these data still presents significant computational challenges because of their noise and sparsity, which are further exacerbated on the level of individual cells and genes. As such, there is a need to develop computational methods that enable scientists to extract systems-level biological insight from noisy high dimensional data.This dissertation introduces DECIPHER, a machine learning framework tailored for network inference in systems biology, with a focus on applications to single-cell RNA sequencing data. Chapter 2 details the DECIPHER algorithm and its implementation for the R computing environment, deciphR, that is designed to reconstruct cell state networks from high-dimensional molecular profiles. Chapter 3 applies DECIPHER to unveil cell-cell interaction networks in the human breast, elucidating how state changes on the cell-level propagate throughout tissue in response to hormonal fluctuations. Chapter 4 extends DECIPHER's application to investigate peripheral immune dysregulation in a rare pediatric autoimmune disease, revealing underlying immune imbalances that persist even in disease remission and potential therapeutic targets. Overall, this dissertation presents a generalizable approach to network inference for systems biology and demonstrates its utility in multiple biological contexts for unravelling cellular coordination in tissue homeostasis and disease.
■590 ▼aSchool code: 0034.
■650 4▼aBioinformatics
■650 4▼aBioengineering
■650 4▼aCellular biology
■650 4▼aGenetics
■653 ▼aMachine learning
■653 ▼aNext generation sequencing
■653 ▼aSystems biology
■653 ▼aGenomics platforms
■690 ▼a0715
■690 ▼a0202
■690 ▼a0379
■690 ▼a0369
■71020▼aUniversity of California, San Francisco▼bBioengineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161270▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
