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Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.
Nat Methods. 2019 Apr;16(4):311-314. doi: 10.1038/s41592-019-0353-7. Epub 2019 Mar 18.
2
A discriminative learning approach to differential expression analysis for single-cell RNA-seq.
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3
A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
PLoS Comput Biol. 2018 Apr 9;14(4):e1006053. doi: 10.1371/journal.pcbi.1006053. eCollection 2018 Apr.
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Deep generative modeling for single-cell transcriptomics.
Nat Methods. 2018 Dec;15(12):1053-1058. doi: 10.1038/s41592-018-0229-2. Epub 2018 Nov 30.
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Data denoising with transfer learning in single-cell transcriptomics.
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6
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.
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dropClust: efficient clustering of ultra-large scRNA-seq data.
Nucleic Acids Res. 2018 Apr 6;46(6):e36. doi: 10.1093/nar/gky007.
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Integrating multiple references for single-cell assignment.
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9
Data Analysis in Single-Cell Transcriptome Sequencing.
Methods Mol Biol. 2018;1754:311-326. doi: 10.1007/978-1-4939-7717-8_18.
10
Using neural networks for reducing the dimensions of single-cell RNA-Seq data.
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2
Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.
Burns Trauma. 2025 May 13;13:tkaf032. doi: 10.1093/burnst/tkaf032. eCollection 2025.
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A robust multi-scale clustering framework for single-cell RNA-seq data analysis.
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Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.
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Single-cell RNA sequencing highlights a significant retinal Müller glial population in dry age-related macular degeneration.
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Single-cell transcriptomics of human skin reveals age-associated specificity of distinct cell populations.
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Advances and applications in single-cell and spatial genomics.
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Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.
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本文引用的文献

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Single-cell RNA-seq denoising using a deep count autoencoder.
Nat Commun. 2019 Jan 23;10(1):390. doi: 10.1038/s41467-018-07931-2.
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Deep generative modeling for single-cell transcriptomics.
Nat Methods. 2018 Dec;15(12):1053-1058. doi: 10.1038/s41592-018-0229-2. Epub 2018 Nov 30.
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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.
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Integrating single-cell transcriptomic data across different conditions, technologies, and species.
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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.
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Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.
Science. 2018 Apr 13;360(6385):176-182. doi: 10.1126/science.aam8999. Epub 2018 Mar 15.
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Mapping the Mouse Cell Atlas by Microwell-Seq.
Cell. 2018 Feb 22;172(5):1091-1107.e17. doi: 10.1016/j.cell.2018.02.001.
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A general and flexible method for signal extraction from single-cell RNA-seq data.
Nat Commun. 2018 Jan 18;9(1):284. doi: 10.1038/s41467-017-02554-5.
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Efficient Generation of Transcriptomic Profiles by Random Composite Measurements.
Cell. 2017 Nov 30;171(6):1424-1436.e18. doi: 10.1016/j.cell.2017.10.023. Epub 2017 Nov 16.
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A single-cell survey of the small intestinal epithelium.
Nature. 2017 Nov 16;551(7680):333-339. doi: 10.1038/nature24489. Epub 2017 Nov 8.

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