581 related articles for article (PubMed ID: 37856458)
41. A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.
Liang X; Cao L; Chen H; Wang L; Wang Y; Fu L; Tan X; Chen E; Ding Y; Tang J
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38168839
[TBL] [Abstract][Full Text] [Related]
42. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
[TBL] [Abstract][Full Text] [Related]
43. scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.
Peng M; Lin B; Zhang J; Zhou Y; Lin B
BMC Genomics; 2024 Mar; 25(1):264. PubMed ID: 38459442
[TBL] [Abstract][Full Text] [Related]
44. Intrinsic entropy model for feature selection of scRNA-seq data.
Li L; Tang H; Xia R; Dai H; Liu R; Chen L
J Mol Cell Biol; 2022 Jun; 14(2):. PubMed ID: 35102420
[TBL] [Abstract][Full Text] [Related]
45. Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data.
Cheng C; Easton J; Rosencrance C; Li Y; Ju B; Williams J; Mulder HL; Pang Y; Chen W; Chen X
Nucleic Acids Res; 2019 Dec; 47(22):e143. PubMed ID: 31566233
[TBL] [Abstract][Full Text] [Related]
46. MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features.
Zhu M; Li C; Lv K; Guo H; Hou R; Tian G; Yang J
Comput Biol Med; 2023 Jun; 159():106873. PubMed ID: 37105115
[TBL] [Abstract][Full Text] [Related]
47. Improving the performance of single-cell RNA-seq data mining based on relative expression orderings.
Chen Y; Zhang H; Sun X
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36528803
[TBL] [Abstract][Full Text] [Related]
48. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.
Hu Y; Hase T; Li HP; Prabhakar S; Kitano H; Ng SK; Ghosh S; Wee LJ
BMC Genomics; 2016 Dec; 17(Suppl 13):1025. PubMed ID: 28155657
[TBL] [Abstract][Full Text] [Related]
49. scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome.
Ren T; Huang S; Liu Q; Wang G
Comput Biol Med; 2023 Jan; 152():106409. PubMed ID: 36512878
[TBL] [Abstract][Full Text] [Related]
50. A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.
Mandal M; Singh PK; Ijaz MF; Shafi J; Sarkar R
Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34451013
[TBL] [Abstract][Full Text] [Related]
51. GNN-based embedding for clustering scRNA-seq data.
Ciortan M; Defrance M
Bioinformatics; 2022 Jan; 38(4):1037-1044. PubMed ID: 34850828
[TBL] [Abstract][Full Text] [Related]
52. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
[TBL] [Abstract][Full Text] [Related]
53. CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data.
Shi Y; Wan J; Zhang X; Yin Y
Comput Biol Med; 2023 Sep; 164():107263. PubMed ID: 37531858
[TBL] [Abstract][Full Text] [Related]
54. Comparison of scRNA-seq data analysis method combinations.
Xu L; Xue T; Ding W; Shen L
Brief Funct Genomics; 2022 Nov; 21(6):433-440. PubMed ID: 36124658
[TBL] [Abstract][Full Text] [Related]
55. scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network.
Wang J; Xia J; Wang H; Su Y; Zheng CH
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36631401
[TBL] [Abstract][Full Text] [Related]
56. Evaluation of single-cell RNAseq labelling algorithms using cancer datasets.
Christensen E; Luo P; Turinsky A; Husić M; Mahalanabis A; Naidas A; Diaz-Mejia JJ; Brudno M; Pugh T; Ramani A; Shooshtari P
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36585784
[TBL] [Abstract][Full Text] [Related]
57. LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.
Lin L; Zhang Y; Qian W; Liu Y; Zhang Y; Lin F; Liu C; Lu G; Sun D; Guo X; Song Y; Song J; Yang C; Li J
Proc Natl Acad Sci U S A; 2022 Feb; 119(5):. PubMed ID: 35086932
[TBL] [Abstract][Full Text] [Related]
58. An optimized graph-based structure for single-cell RNA-seq cell-type classification based on non-linear dimension reduction.
Abadi SAR; Laghaee SP; Koohi S
BMC Genomics; 2023 May; 24(1):227. PubMed ID: 37127578
[TBL] [Abstract][Full Text] [Related]
59. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
[TBL] [Abstract][Full Text] [Related]
60. scGCL: an imputation method for scRNA-seq data based on graph contrastive learning.
Xiong Z; Luo J; Shi W; Liu Y; Xu Z; Wang B
Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36825817
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
