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Quantitative and Computational Approaches in Translational Biophysics Applications- [electronic resource]
Quantitative and Computational Approaches in Translational Biophysics Applications- [electronic resource]
상세정보
- 자료유형
- 학위논문
- Control Number
- 0016932325
- International Standard Book Number
- 9798379613020
- Dewey Decimal Classification Number
- 616
- Main Entry-Personal Name
- Greenfield, Daniel A.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Harvard University., 2023
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- Physical Description
- 1 online resource(222 p.)
- General Note
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- General Note
- Advisor: Evans, Conor L.;Wong, Wesley.
- Dissertation Note
- Thesis (Ph.D.)--Harvard University, 2023.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Summary, Etc.
- 요약Experimental and theoretical biophysical applications intertwine concepts from Computational, Life, Mathematical, and Physical sciences to expand the knowledge base and societal impact of various scientific advances. Interdisciplinary biophysics applications are generally unified by the underlying characteristics of the data they capture. Specifically, data created and collected in such applications have common characteristics of spanning space, time, and biological scales of life, rendering scientists with complex, high-dimensionality data to extract insights and draw conclusions. The harnessing of this data, development of widely applicable analytical tools, and transformation from raw data to tangible information is a challenge well suited for approaches based on statistical principles and advanced machine learning to tackle.This thesis covers the conceptualization, implementation, and application of quantitative approaches to analyzing biophysical data in biomedical contexts. Specifically, these projects span various biological scales and clinical levels, from pre-clinical data from ex-vivo samples and in-vivo animal models, through human patient data analyzed both post-hoc and approaching real-time. A primary focus of this work is the development and application of machine learning toolkits for predictive analysis, from data collection and engineering pipelines through model implementation, training, and analysis, using deep learning and model interpretability approaches to turn multidimensional biophysical data into actionable information. The main takeaway from this body of work is a set of new quantitative methods that outperform existing baselines for unique and novel, yet clinically relevant, biophysical datasets, unified by their translational potential and impact towards therapeutics and/or diagnostics.
- Subject Added Entry-Topical Term
- Medical imaging.
- Subject Added Entry-Topical Term
- Applied mathematics.
- Subject Added Entry-Topical Term
- Biophysics.
- Index Term-Uncontrolled
- Physical sciences
- Index Term-Uncontrolled
- Biophysics applications
- Index Term-Uncontrolled
- Biophysical data
- Index Term-Uncontrolled
- Machine learning
- Index Term-Uncontrolled
- Animal models
- Added Entry-Corporate Name
- Harvard University Biophysics
- Host Item Entry
- Dissertations Abstracts International. 84-12B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:642044
MARC
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■1001 ▼aGreenfield, Daniel A.▼0(orcid)0000-0002-1369-8124
■24510▼aQuantitative and Computational Approaches in Translational Biophysics Applications▼h[electronic resource]
■260 ▼a[S.l.]▼bHarvard University. ▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a1 online resource(222 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Evans, Conor L.;Wong, Wesley.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aExperimental and theoretical biophysical applications intertwine concepts from Computational, Life, Mathematical, and Physical sciences to expand the knowledge base and societal impact of various scientific advances. Interdisciplinary biophysics applications are generally unified by the underlying characteristics of the data they capture. Specifically, data created and collected in such applications have common characteristics of spanning space, time, and biological scales of life, rendering scientists with complex, high-dimensionality data to extract insights and draw conclusions. The harnessing of this data, development of widely applicable analytical tools, and transformation from raw data to tangible information is a challenge well suited for approaches based on statistical principles and advanced machine learning to tackle.This thesis covers the conceptualization, implementation, and application of quantitative approaches to analyzing biophysical data in biomedical contexts. Specifically, these projects span various biological scales and clinical levels, from pre-clinical data from ex-vivo samples and in-vivo animal models, through human patient data analyzed both post-hoc and approaching real-time. A primary focus of this work is the development and application of machine learning toolkits for predictive analysis, from data collection and engineering pipelines through model implementation, training, and analysis, using deep learning and model interpretability approaches to turn multidimensional biophysical data into actionable information. The main takeaway from this body of work is a set of new quantitative methods that outperform existing baselines for unique and novel, yet clinically relevant, biophysical datasets, unified by their translational potential and impact towards therapeutics and/or diagnostics.
■590 ▼aSchool code: 0084.
■650 4▼aMedical imaging.
■650 4▼aApplied mathematics.
■650 4▼aBiophysics.
■653 ▼aPhysical sciences
■653 ▼aBiophysics applications
■653 ▼aBiophysical data
■653 ▼aMachine learning
■653 ▼aAnimal models
■690 ▼a0574
■690 ▼a0364
■690 ▼a0800
■690 ▼a0786
■71020▼aHarvard University▼bBiophysics.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932325▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
