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Geometric Learning of Biomolecular Structure- [electronic resource]
![Geometric Learning of Biomolecular Structure - [electronic resource]](/Users/Baul/Images/book.png)
Geometric Learning of Biomolecular Structure- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101306
- ISBN
- 9798379869656
- DDC
- 006
- 서명/저자
- Geometric Learning of Biomolecular Structure - [electronic resource]
- 발행사항
- [S.l.]: : Stanford University., 2021
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2021
- 형태사항
- 1 online resource(98 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Dror, Ron;Altman, Russ;Kundaje, Anshul.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2021.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The shape of a macromolecule such as a protein, RNA, or DNA, is intrinsically linked to its biological function. Better reasoning about these shapes may unlock new scientific discoveries in human health and open a path towards the rational design of novel medicines and materials. I demonstrate the potential of machine learning in this area by discussing the design of a new class of neural networks that are geometric in nature: they exploit the three-dimensional arrangement of atoms-thereby modeling the underlying physical processes of molecular structure-to generalize to new and unseen molecules. These results point to machine learning as an area of great promise for structural biology.
- 일반주제명
- Carbon.
- 일반주제명
- Neural networks.
- 일반주제명
- Symmetry.
- 일반주제명
- Design.
- 일반주제명
- Amino acids.
- 일반주제명
- Information processing.
- 일반주제명
- Engineering.
- 일반주제명
- Biology.
- 일반주제명
- Interfaces.
- 일반주제명
- Statistics.
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101306
■006m o d
■007cr#unu||||||||
■020 ▼a9798379869656
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■035 ▼a(MiAaPQ)STANFORDqs870pj6857
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a006
■1001 ▼aTownshend, Raphael John Lamarre.
■24510▼aGeometric Learning of Biomolecular Structure▼h[electronic resource]
■260 ▼a[S.l.]:▼bStanford University. ▼c2021
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2021
■300 ▼a1 online resource(98 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Dror, Ron;Altman, Russ;Kundaje, Anshul.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2021.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe shape of a macromolecule such as a protein, RNA, or DNA, is intrinsically linked to its biological function. Better reasoning about these shapes may unlock new scientific discoveries in human health and open a path towards the rational design of novel medicines and materials. I demonstrate the potential of machine learning in this area by discussing the design of a new class of neural networks that are geometric in nature: they exploit the three-dimensional arrangement of atoms-thereby modeling the underlying physical processes of molecular structure-to generalize to new and unseen molecules. These results point to machine learning as an area of great promise for structural biology.
■590 ▼aSchool code: 0212.
■650 4▼aCarbon.
■650 4▼aNeural networks.
■650 4▼aSymmetry.
■650 4▼aDesign.
■650 4▼aAmino acids.
■650 4▼aInformation processing.
■650 4▼aEngineering.
■650 4▼aBiology.
■650 4▼aInterfaces.
■650 4▼aStatistics.
■690 ▼a0389
■690 ▼a0537
■690 ▼a0306
■690 ▼a0800
■690 ▼a0463
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2021
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933573▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
