期刊
NATURE REVIEWS MOLECULAR CELL BIOLOGY
卷 23, 期 5, 页码 303-304出版社
NATURE PORTFOLIO
DOI: 10.1038/s41580-022-00466-x
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资金
- National Institutes of Health [R35-GM126985, R01-GM131399]
Deep learning has great potential in single-cell data analysis, but there are still many challenges and possible new developments to be explored. In this commentary, the progress, limitations, best practices, and outlook of adapting deep learning methods for analyzing single-cell data are considered.
Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored. In this commentary, we consider the progress, limitations, best practices and outlook of adapting deep learning methods for analysing single-cell data.
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