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A robust and interpretable end-to-end deep learning model for cytometry data
Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry...
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| 出版年: | Proc Natl Acad Sci U S A |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
National Academy of Sciences
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7474669/ https://ncbi.nlm.nih.gov/pubmed/32801215 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.2003026117 |
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