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Unsupervised Machine Learning Algorithms to Characterize Single-Cell Heterogeneity and Perturbation Response
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Unsupervised Machine Learning Algorithms to Characterize Single-Cell Heterogeneity and Perturbation Response. |
| 개인저자 | Burkhardt, Daniel Bernard. |
| 단체저자명 | Yale University. Genetics. |
| 발행사항 | [S.l.]: Yale University., 2021. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2021. |
| 형태사항 | 164 p. |
| 기본자료 저록 | Dissertations Abstracts International 83-02B. Dissertation Abstract International |
| ISBN | 9798522999056 |
| 학위논문주기 | Thesis (Ph.D.)--Yale University, 2021. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Advisor: Krishnaswamy, Smita. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 일반주제명 | Genetics. Biology. Computer science. Artificial intelligence. Deep learning. Datasets. Signal processing. Data analysis. Noise. Clustering. Genes. Mutagenesis. Visualization. Neurons. Principal components analysis. Fibroblasts. Graph representations. Neural networks. Quantitative analysis. Methods. Algorithms. Geometry. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
