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Hypothesis-Driven Training for DNA Methylation Clocks and Feature Rectification for Linear Model Coherence to Detect Inflammaging
Hypothesis-Driven Training for DNA Methylation Clocks and Feature Rectification for Linear Model Coherence to Detect Inflammaging
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105102
- ISBN
- 9798297601437
- DDC
- 610
- 저자명
- Skinner, Colin.
- 서명/저자
- Hypothesis-Driven Training for DNA Methylation Clocks and Feature Rectification for Linear Model Coherence to Detect Inflammaging
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 83 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 주기사항
- Advisor: Conboy, Irina M.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약Biological age estimation from DNA methylation and the identification of relevant biomarkers is an active research problem that has predominantly been tackled using penalized regression (e.g., elastic net). Such models are commonly used to select small subsets of CpG probes from hundreds of thousands of candidates and to regularize training. Here, I show that feature sets discovered by these approaches can lack biological interpretability and relevance in first‑ and next‑generation DNA methylation clocks, and I clarify why these procedures can systematically exclude biomarkers of aging and age‑related disease. In contrast to the assumption that regularized linear regression is required to prevent overfitting, I demonstrate that hypothesis‑driven selection of biologically relevant features, combined with ordinary least squares (OLS), yields accurate, well‑calibrated, and generalizable clocks with high interpretability. I further show that disease‑associated shifts in CpG methylation interacting with opposite‑signed model weights can cancel at the aggregate-an incoherence effect that reduces resolution between health and inflammaging. Lastly, I introduce feature rectification, which aligns these shifts to restore coherent signal aggregation and improves separation of DNAm‑age predictions for healthy individuals versus patients with chronic inflammatory diseases.
- 일반주제명
- Bioengineering
- 일반주제명
- Cellular biology
- 일반주제명
- Molecular biology
- 일반주제명
- Genetics
- 키워드
- DNA methylation
- 키워드
- Inflammaging
- 기타저자
- University of California, Berkeley Bioengineering
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798297601437
■035 ▼a(MiAaPQ)AAI32236083
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aSkinner, Colin.
■24510▼aHypothesis-Driven Training for DNA Methylation Clocks and Feature Rectification for Linear Model Coherence to Detect Inflammaging
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a83 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Conboy, Irina M.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aBiological age estimation from DNA methylation and the identification of relevant biomarkers is an active research problem that has predominantly been tackled using penalized regression (e.g., elastic net). Such models are commonly used to select small subsets of CpG probes from hundreds of thousands of candidates and to regularize training. Here, I show that feature sets discovered by these approaches can lack biological interpretability and relevance in first‑ and next‑generation DNA methylation clocks, and I clarify why these procedures can systematically exclude biomarkers of aging and age‑related disease. In contrast to the assumption that regularized linear regression is required to prevent overfitting, I demonstrate that hypothesis‑driven selection of biologically relevant features, combined with ordinary least squares (OLS), yields accurate, well‑calibrated, and generalizable clocks with high interpretability. I further show that disease‑associated shifts in CpG methylation interacting with opposite‑signed model weights can cancel at the aggregate-an incoherence effect that reduces resolution between health and inflammaging. Lastly, I introduce feature rectification, which aligns these shifts to restore coherent signal aggregation and improves separation of DNAm‑age predictions for healthy individuals versus patients with chronic inflammatory diseases.
■590 ▼aSchool code: 0028.
■650 4▼aBioengineering
■650 4▼aCellular biology
■650 4▼aMolecular biology
■650 4▼aGenetics
■653 ▼aDNA methylation
■653 ▼aElastic net regression
■653 ▼aEpigenetic clocks
■653 ▼aFeature rectification
■653 ▼aInflammaging
■653 ▼aOrdinary least squares
■690 ▼a0202
■690 ▼a0379
■690 ▼a0369
■690 ▼a0307
■71020▼aUniversity of California, Berkeley▼bBioengineering.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359325▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
