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Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks
Single-cell RNA sequencing technology promotes the profiling of single-cell transcriptomes at an unprecedented throughput and resolution. However, in scRNA-seq studies, only a low amount of sequenced mRNA in each cell leads to missing detection for a portion of mRNA molecules, i.e. the dropout probl...
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| I publikationen: | iScience |
|---|---|
| Huvudupphovsmän: | , , , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
Elsevier
2021
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8091052/ https://ncbi.nlm.nih.gov/pubmed/33997678 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.isci.2021.102393 |
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