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Drawing Biological Understanding From Machine Learning
Drawing Biological Understanding From Machine Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152754
- ISBN
- 9798384455905
- DDC
- 004
- 저자명
- Yang, Forest.
- 서명/저자
- Drawing Biological Understanding From Machine Learning
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 105 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: El Ghaoui, Laurent.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약Large biological data, such as medical imaging and single-cell level genomic data, are rich sources of biological information. Machine learning is a tool to extract that information into a usable form, whether it be predictions for some prediction task or insights drawn from the model. We explore three applications of machine learning to biology. One is on using deep learning to perform metastatic cancer prognosis from CT images by predicting lesion-level risks. We use the lesion-level risks to show that the model captures clinically known indicators of risk. Next, we utilize the DeepLIFT and TF-MoDISco neural network interpretation techniques to understand how DNA shape affects transcription factor binding. Overall, we find that sequence features are more important for distinguishing bound sites, but that shape features can modulate binding affinity. Finally, we the test the CellOracle and SCENIC+ gene regulatory network inference frameworks in the context of reprogramming fibroblasts to pluripotent cells, to prioritize key factors in reprogramming and recover their effects on differentiation.
- 일반주제명
- Computer science
- 일반주제명
- Molecular biology
- 일반주제명
- Genetics
- 키워드
- Cancer prognosis
- 키워드
- Machine learning
- 기타저자
- University of California, Berkeley Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798384455905
■035 ▼a(MiAaPQ)AAI31555725
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aYang, Forest.
■24510▼aDrawing Biological Understanding From Machine Learning
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a105 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: El Ghaoui, Laurent.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aLarge biological data, such as medical imaging and single-cell level genomic data, are rich sources of biological information. Machine learning is a tool to extract that information into a usable form, whether it be predictions for some prediction task or insights drawn from the model. We explore three applications of machine learning to biology. One is on using deep learning to perform metastatic cancer prognosis from CT images by predicting lesion-level risks. We use the lesion-level risks to show that the model captures clinically known indicators of risk. Next, we utilize the DeepLIFT and TF-MoDISco neural network interpretation techniques to understand how DNA shape affects transcription factor binding. Overall, we find that sequence features are more important for distinguishing bound sites, but that shape features can modulate binding affinity. Finally, we the test the CellOracle and SCENIC+ gene regulatory network inference frameworks in the context of reprogramming fibroblasts to pluripotent cells, to prioritize key factors in reprogramming and recover their effects on differentiation.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science
■650 4▼aMolecular biology
■650 4▼aGenetics
■653 ▼aCancer prognosis
■653 ▼aComputational biology
■653 ▼aGene regulatory networks
■653 ▼aMachine learning
■653 ▼aTranscription factor
■690 ▼a0984
■690 ▼a0369
■690 ▼a0307
■71020▼aUniversity of California, Berkeley▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163797▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
