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Manifold Learning in Structural Biology: Docking, Folding, and Simulation
Manifold Learning in Structural Biology: Docking, Folding, and Simulation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152111
- ISBN
- 9798384212102
- DDC
- 004
- 저자명
- Sha, Congzhou M.
- 서명/저자
- Manifold Learning in Structural Biology: Docking, Folding, and Simulation
- 발행사항
- [Sl] : The Pennsylvania State University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 306 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Dokholyan, Nikolay V.
- 학위논문주기
- Thesis (Ph.D.)--The Pennsylvania State University, 2024.
- 초록/해제
- 요약Machine learning and neural networks are used in many areas of science to perform data fitting, automation, and inference. However, these methods are often used as black boxes, leading to uncertainty on the scientist's part in their accuracy and robustness. Many problems in structural biology may be addressed using machine learning techniques, from virtual drug docking and RNA 3D structure to molecular dynamics. In order to use machine learning effectively, I have constructed principled and rational methods based on physics, geometry, and symmetry to address problems in structural biology. By taking advantage of fundamental mathematical properties in the problems under consideration, we are able to (1) develop efficient, accurate, and trustworthy inferential models and (2) abstract away from the details of the problem and develop general methods applicable to other areas of science. The choices I have made in constructing these methods reflect our current understanding of deep learning, the structure of scientific data, and the mathematical principles underlying our models of Nature.
- 일반주제명
- Quantum computing
- 일반주제명
- Biology
- 일반주제명
- Statistical mechanics
- 일반주제명
- Quantum physics
- 일반주제명
- Neural networks
- 일반주제명
- Markov analysis
- 일반주제명
- Symmetry
- 일반주제명
- Computational physics
- 일반주제명
- Statistical physics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aSha, Congzhou M.
■24510▼aManifold Learning in Structural Biology: Docking, Folding, and Simulation
■260 ▼a[Sl]▼bThe Pennsylvania State University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a306 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Dokholyan, Nikolay V.
■5021 ▼aThesis (Ph.D.)--The Pennsylvania State University, 2024.
■520 ▼aMachine learning and neural networks are used in many areas of science to perform data fitting, automation, and inference. However, these methods are often used as black boxes, leading to uncertainty on the scientist's part in their accuracy and robustness. Many problems in structural biology may be addressed using machine learning techniques, from virtual drug docking and RNA 3D structure to molecular dynamics. In order to use machine learning effectively, I have constructed principled and rational methods based on physics, geometry, and symmetry to address problems in structural biology. By taking advantage of fundamental mathematical properties in the problems under consideration, we are able to (1) develop efficient, accurate, and trustworthy inferential models and (2) abstract away from the details of the problem and develop general methods applicable to other areas of science. The choices I have made in constructing these methods reflect our current understanding of deep learning, the structure of scientific data, and the mathematical principles underlying our models of Nature.
■590 ▼aSchool code: 0176.
■650 4▼aQuantum computing
■650 4▼aBiology
■650 4▼aStatistical mechanics
■650 4▼aQuantum physics
■650 4▼aNeural networks
■650 4▼aMarkov analysis
■650 4▼aSymmetry
■650 4▼aComputational physics
■650 4▼aStatistical physics
■690 ▼a0599
■690 ▼a0306
■690 ▼a0800
■690 ▼a0216
■690 ▼a0796
■690 ▼a0217
■71020▼aThe Pennsylvania State University.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0176
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162914▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
