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Deep-learning methods for unveiling large-scale single-cell transcriptomes
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DOI：10.20892/j.issn.2095-3941.2023.0436.png)
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Abstract
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The rapidly evolving realm of single-cell transcriptomics offers vital new perspectives into the understanding of intra- and inter-cellular molecular dynamics governing development, physiology, and pathogenesis. Deep learning, a recent artificial intelligence advance with a promising application for big data, has demonstrated potential in the field of single-cell analysis1. Deep learning exhibits flexibility in extracting informative feaing (scRNA-seq) data and enhances downstream analyses. We surveyed recent deep-learning methods that advance single-cell analysis and offer a glimpse into what the future holds.
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