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本文引用的文献

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Deep Learning Implicitly Handles Tissue Specific Phenomena to Predict Tumor DNA Accessibility and Immune Activity.
iScience. 2019 Oct 25;20:119-136. doi: 10.1016/j.isci.2019.09.018. Epub 2019 Sep 14.
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Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq.
Nucleic Acids Res. 2019 Nov 4;47(19):e121. doi: 10.1093/nar/gkz716.
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A comparison of single-cell trajectory inference methods.
Nat Biotechnol. 2019 May;37(5):547-554. doi: 10.1038/s41587-019-0071-9. Epub 2019 Apr 1.
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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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Joint profiling of chromatin accessibility and gene expression in thousands of single cells.
Science. 2018 Sep 28;361(6409):1380-1385. doi: 10.1126/science.aau0730. Epub 2018 Aug 30.
8
Genome-wide prediction of DNase I hypersensitivity using gene expression.
Nat Commun. 2017 Oct 19;8(1):1038. doi: 10.1038/s41467-017-01188-x.
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Single-cell regulome data analysis by SCRAT.
Bioinformatics. 2017 Sep 15;33(18):2930-2932. doi: 10.1093/bioinformatics/btx315.
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TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.
Nucleic Acids Res. 2016 Jul 27;44(13):e117. doi: 10.1093/nar/gkw430. Epub 2016 May 13.

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