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Cited by in F6Publishing
For: Xie P, Gao M, Wang C, Zhang J, Noel P, Yang C, Von Hoff D, Han H, Zhang MQ, Lin W. SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles. Nucleic Acids Res. 2019;47:e48. [PMID: 30799483 DOI: 10.1093/nar/gkz116] [Cited by in Crossref: 25] [Cited by in F6Publishing: 16] [Article Influence: 12.5] [Reference Citation Analysis]
Number Citing Articles
1 Zhong J, Lin W. Use of SuperCT for Enhanced Characterization of Single-Cell Transcriptomic Profiles. Methods Mol Biol 2020;2117:169-77. [PMID: 31960378 DOI: 10.1007/978-1-0716-0301-7_9] [Reference Citation Analysis]
2 Chen S, Yan G, Zhang W, Li J, Jiang R, Lin Z. RA3 is a reference-guided approach for epigenetic characterization of single cells. Nat Commun 2021;12:2177. [PMID: 33846355 DOI: 10.1038/s41467-021-22495-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
3 Zeng T, Dai H. Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity. Front Genet 2019;10:629. [PMID: 31354786 DOI: 10.3389/fgene.2019.00629] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
4 Sánchez-Corrales YE, Pohle RVC, Castellano S, Giustacchini A. Taming Cell-to-Cell Heterogeneity in Acute Myeloid Leukaemia With Machine Learning. Front Oncol 2021;11:666829. [PMID: 33996595 DOI: 10.3389/fonc.2021.666829] [Reference Citation Analysis]
5 Wang Z, Ding H, Zou Q. Identifying cell types to interpret scRNA-seq data: how, why and more possibilities. Briefings in Functional Genomics 2020;19:286-91. [DOI: 10.1093/bfgp/elaa003] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
6 Gao M, Ling M, Tang X, Wang S, Xiao X, Qiao Y, Yang W, Yu R. Comparison of high-throughput single-cell RNA sequencing data processing pipelines. Brief Bioinform 2021;22:bbaa116. [PMID: 34020539 DOI: 10.1093/bib/bbaa116] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
7 Miao Z, Moreno P, Huang N, Papatheodorou I, Brazma A, Teichmann SA. Putative cell type discovery from single-cell gene expression data. Nat Methods 2020;17:621-8. [DOI: 10.1038/s41592-020-0825-9] [Cited by in Crossref: 20] [Cited by in F6Publishing: 14] [Article Influence: 20.0] [Reference Citation Analysis]
8 Pasquini G, Rojo Arias JE, Schäfer P, Busskamp V. Automated methods for cell type annotation on scRNA-seq data. Comput Struct Biotechnol J 2021;19:961-9. [PMID: 33613863 DOI: 10.1016/j.csbj.2021.01.015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
9 Lin H, Xue X, Wang X, Dang S, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. AIG 2020;1:19-29. [DOI: 10.35712/aig.v1.i1.19] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Yu X, Abbas-Aghababazadeh F, Chen YA, Fridley BL. Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments. Methods Mol Biol 2021;2194:143-75. [PMID: 32926366 DOI: 10.1007/978-1-0716-0849-4_9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Lin W, Noel P, Borazanci EH, Lee J, Amini A, Han IW, Heo JS, Jameson GS, Fraser C, Steinbach M, Woo Y, Fong Y, Cridebring D, Von Hoff DD, Park JO, Han H. Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions. Genome Med 2020;12:80. [PMID: 32988401 DOI: 10.1186/s13073-020-00776-9] [Cited by in Crossref: 14] [Cited by in F6Publishing: 8] [Article Influence: 14.0] [Reference Citation Analysis]
12 Jia S, Hu P. ChrNet: A re-trainable chromosome-based 1D convolutional neural network for predicting immune cell types. Genomics 2021;113:2023-31. [PMID: 33932523 DOI: 10.1016/j.ygeno.2021.04.037] [Reference Citation Analysis]
13 Forcato M, Romano O, Bicciato S. Computational methods for the integrative analysis of single-cell data. Brief Bioinform 2021;22:20-9. [PMID: 32363378 DOI: 10.1093/bib/bbaa042] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 11.0] [Reference Citation Analysis]
14 Ma F, Pellegrini M. ACTINN: automated identification of cell types in single cell RNA sequencing. Bioinformatics 2020;36:533-8. [PMID: 31359028 DOI: 10.1093/bioinformatics/btz592] [Cited by in Crossref: 10] [Cited by in F6Publishing: 21] [Article Influence: 10.0] [Reference Citation Analysis]
15 de Kanter JK, Lijnzaad P, Candelli T, Margaritis T, Holstege FCP. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res 2019;47:e95. [PMID: 31226206 DOI: 10.1093/nar/gkz543] [Cited by in Crossref: 56] [Cited by in F6Publishing: 36] [Article Influence: 28.0] [Reference Citation Analysis]
16 Lei Y, Tang R, Xu J, Wang W, Zhang B, Liu J, Yu X, Shi S. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol 2021;14:91. [PMID: 34108022 DOI: 10.1186/s13045-021-01105-2] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]