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On the Quantification of Aging : 衰老的量化研究
On the Quantification of Aging : 衰老的量化研究
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103351
- ISBN
- 9798280720749
- DDC
- 590
- 저자명
- Ying, Kejun.
- 서명/저자
- On the Quantification of Aging : 衰老的量化研究
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 207 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Gladyshev, Vadim N.;Manning, Brendan.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약This dissertation explores the complex biological process of aging through multiple methodological lenses, from molecular mechanisms to population-level analyses. As global demographics shift toward an increasingly older population, understanding aging mechanisms and developing interventions to extend healthy lifespan have become critical scientific priorities. Despite chronological age being the strongest risk factor for many diseases, individuals age at different rates, suggesting that chronological age alone is an insufficient measure of biological aging. This research addresses key challenges in aging research through a multifaceted approach combining traditional genetic and epidemiological analyses with advanced computational methods. The dissertation investigates causal relationships between aging and disease, develops causality-enriched epigenetic clocks, examines the role of germline mutations in exceptional longevity, constructs high-dimensional representations of aging, develops foundation models for analyzing complex aging-related data, establishes standardized frameworks for biomarker evaluation, and creates a unified theoretical definition of biological age. By pursuing these objectives, this work aims to contribute significantly to our understanding of the aging process and provide tools and frameworks that can accelerate research in this field.
- 일반주제명
- Systematic biology
- 일반주제명
- Aging
- 일반주제명
- Bioinformatics
- 키워드
- Biomarkers
- 키워드
- Genetics
- 키워드
- Machine learning
- 키워드
- Systems biology
- 기타저자
- Harvard University Biological Sciences in Public Health
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280720749
■035 ▼a(MiAaPQ)AAI31997929
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a590
■1001 ▼aYing, Kejun.▼0(orcid)0000-0002-1791-6176
■24510▼aOn the Quantification of Aging ▼b衰老的量化研究
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a207 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Gladyshev, Vadim N.;Manning, Brendan.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aThis dissertation explores the complex biological process of aging through multiple methodological lenses, from molecular mechanisms to population-level analyses. As global demographics shift toward an increasingly older population, understanding aging mechanisms and developing interventions to extend healthy lifespan have become critical scientific priorities. Despite chronological age being the strongest risk factor for many diseases, individuals age at different rates, suggesting that chronological age alone is an insufficient measure of biological aging. This research addresses key challenges in aging research through a multifaceted approach combining traditional genetic and epidemiological analyses with advanced computational methods. The dissertation investigates causal relationships between aging and disease, develops causality-enriched epigenetic clocks, examines the role of germline mutations in exceptional longevity, constructs high-dimensional representations of aging, develops foundation models for analyzing complex aging-related data, establishes standardized frameworks for biomarker evaluation, and creates a unified theoretical definition of biological age. By pursuing these objectives, this work aims to contribute significantly to our understanding of the aging process and provide tools and frameworks that can accelerate research in this field.
■590 ▼aSchool code: 0084.
■650 4▼aSystematic biology
■650 4▼aAging
■650 4▼aBioinformatics
■653 ▼aBiomarkers
■653 ▼aGenetics
■653 ▼aMachine learning
■653 ▼aSystems biology
■690 ▼a0423
■690 ▼a0493
■690 ▼a0715
■690 ▼a0800
■71020▼aHarvard University▼bBiological Sciences in Public Health.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357367▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
