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Machine Learning Insights into the 3D Genome: Diversity and Gene Regulation in Human Populations
Machine Learning Insights into the 3D Genome: Diversity and Gene Regulation in Human Populations
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152723
- ISBN
- 9798384077695
- DDC
- 574
- 서명/저자
- Machine Learning Insights into the 3D Genome: Diversity and Gene Regulation in Human Populations
- 발행사항
- [Sl] : University of California, San Francisco, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 180 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Capra, John A.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2024.
- 초록/해제
- 요약The three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation, influencing interactions between genes and regulatory elements. Despite significant progress in genomics, the diversity of 3D chromatin contact patterns across human populations remains underexplored. This dissertation uses machine learning to predict 3D chromatin contact maps from genome sequences, revealing new insights into genome architecture among diverse populations. In Chapter 1, I provide a literature review and overview of human population and regulatory genetics in relationship to the 3D genome with a focus on machine learning techniques for studying each. In Chapter 2 I present the results of my study using a machine learning model to predict 3D genome for thousands of individuals, uncovering substantial 3D genomic diversity, particularly within African populations. It also identifies regions where 3D divergence occurs independently of sequence variation, especially in areas under low functional constraint. These findings underscore the importance of considering 3D genome organization in understanding gene regulation and its implications for health and disease.
- 일반주제명
- Bioinformatics
- 일반주제명
- Genetics
- 일반주제명
- Evolution & development
- 키워드
- 3D genome
- 키워드
- Machine learning
- 기타저자
- University of California, San Francisco Biological and Medical Informatics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798384077695
■035 ▼a(MiAaPQ)AAI31489884
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aGilbertson, Erin Nicole.▼0(orcid)0000-0002-2426-9966
■24510▼aMachine Learning Insights into the 3D Genome: Diversity and Gene Regulation in Human Populations
■260 ▼a[Sl]▼bUniversity of California, San Francisco▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a180 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Capra, John A.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2024.
■520 ▼aThe three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation, influencing interactions between genes and regulatory elements. Despite significant progress in genomics, the diversity of 3D chromatin contact patterns across human populations remains underexplored. This dissertation uses machine learning to predict 3D chromatin contact maps from genome sequences, revealing new insights into genome architecture among diverse populations. In Chapter 1, I provide a literature review and overview of human population and regulatory genetics in relationship to the 3D genome with a focus on machine learning techniques for studying each. In Chapter 2 I present the results of my study using a machine learning model to predict 3D genome for thousands of individuals, uncovering substantial 3D genomic diversity, particularly within African populations. It also identifies regions where 3D divergence occurs independently of sequence variation, especially in areas under low functional constraint. These findings underscore the importance of considering 3D genome organization in understanding gene regulation and its implications for health and disease.
■590 ▼aSchool code: 0034.
■650 4▼aBioinformatics
■650 4▼aGenetics
■650 4▼aEvolution & development
■653 ▼a3D genome
■653 ▼aEvolutionary genomics
■653 ▼aMachine learning
■653 ▼aPopulation genetics
■653 ▼aRegulatory genomics
■690 ▼a0715
■690 ▼a0369
■690 ▼a0412
■690 ▼a0800
■71020▼aUniversity of California, San Francisco▼bBiological and Medical Informatics.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163549▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
