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Unsupervised Machine Learning Algorithms to Characterize Single-Cell Heterogeneity and Perturbation Response
Unsupervised Machine Learning Algorithms to Characterize Single-Cell Heterogeneity and Perturbation Response
상세정보
- 자료유형
- 학위논문파일 국외
- ISBN
- 9798522999056
- DDC
- 575
- 서명/저자
- Unsupervised Machine Learning Algorithms to Characterize Single-Cell Heterogeneity and Perturbation Response
- 발행사항
- [Sl] : Yale University, 2021
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2021
- 형태사항
- 164 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
- 주기사항
- Advisor: Krishnaswamy, Smita.
- 학위논문주기
- Thesis (Ph.D.)--Yale University, 2021.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 일반주제명
- Genetics
- 일반주제명
- Biology
- 일반주제명
- Computer science
- 일반주제명
- Artificial intelligence
- 일반주제명
- Deep learning
- 일반주제명
- Datasets
- 일반주제명
- Signal processing
- 일반주제명
- Data analysis
- 일반주제명
- Noise
- 일반주제명
- Clustering
- 일반주제명
- Genes
- 일반주제명
- Mutagenesis
- 일반주제명
- Visualization
- 일반주제명
- Neurons
- 일반주제명
- Fibroblasts
- 일반주제명
- Graph representations
- 일반주제명
- Neural networks
- 일반주제명
- Quantitative analysis
- 일반주제명
- Methods
- 일반주제명
- Algorithms
- 일반주제명
- Geometry
- 키워드
- Genomics
- 키워드
- Machine learning
- 키워드
- Manifold
- 기타저자
- Yale University Genetics
- 기본자료저록
- Dissertations Abstracts International. 83-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008220131s2021 us c eng d■020 ▼a9798522999056
■035 ▼a(MiAaPQ)AAI28322261
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a575
■1001 ▼aBurkhardt, Daniel Bernard.
■24510▼aUnsupervised Machine Learning Algorithms to Characterize Single-Cell Heterogeneity and Perturbation Response
■260 ▼a[Sl]▼bYale University▼c2021
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2021
■300 ▼a164 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 83-02, Section: B.
■500 ▼aAdvisor: Krishnaswamy, Smita.
■5021 ▼aThesis (Ph.D.)--Yale University, 2021.
■506 ▼aThis item must not be sold to any third party vendors.
■590 ▼aSchool code: 0265.
■650 4▼aGenetics
■650 4▼aBiology
■650 4▼aComputer science
■650 4▼aArtificial intelligence
■650 4▼aDeep learning
■650 4▼aDatasets
■650 4▼aSignal processing
■650 4▼aData analysis
■650 4▼aNoise
■650 4▼aClustering
■650 4▼aGenes
■650 4▼aMutagenesis
■650 4▼aVisualization
■650 4▼aNeurons
■650 4▼aPrincipal components analysis
■650 4▼aFibroblasts
■650 4▼aGraph representations
■650 4▼aNeural networks
■650 4▼aQuantitative analysis
■650 4▼aMethods
■650 4▼aAlgorithms
■650 4▼aGeometry
■653 ▼aComputational biology
■653 ▼aGenomics
■653 ▼aGraph signal processing
■653 ▼aMachine learning
■653 ▼aManifold
■653 ▼aSingle-cell heterogeniety
■690 ▼a0369
■690 ▼a0984
■690 ▼a0800
■690 ▼a0306
■71020▼aYale University▼bGenetics.
■7730 ▼tDissertations Abstracts International▼g83-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0265
■791 ▼aPh.D.
■792 ▼a2021
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16051413▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202202▼f2022
