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Integration and transfer learning of single-cell transcriptomes via cFIT
Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a...
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| Vydáno v: | Proc Natl Acad Sci U S A |
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| Hlavní autoři: | , , , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
National Academy of Sciences
2021
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7958425/ https://ncbi.nlm.nih.gov/pubmed/33658382 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.2024383118 |
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