193 related articles for article (PubMed ID: 36157595)
21. Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis.
Hu J; Li X; Hu G; Lyu Y; Susztak K; Li M
Nat Mach Intell; 2020 Oct; 2(10):607-618. PubMed ID: 33817554
[TBL] [Abstract][Full Text] [Related]
22. scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data.
Zeng P; Ma Y; Lin Z
Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36383176
[TBL] [Abstract][Full Text] [Related]
23. CAMML with the Integration of Marker Proteins (ChIMP).
Schiebout C; Frost HR
Bioinformatics; 2022 Nov; 38(23):5206-5213. PubMed ID: 36214642
[TBL] [Abstract][Full Text] [Related]
24. Multi-View Clustering With Graph Learning for scRNA-Seq Data.
Wu W; Zhang W; Hou W; Ma X
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3535-3546. PubMed ID: 37486829
[TBL] [Abstract][Full Text] [Related]
25. CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data.
Xu J; Zhang A; Liu F; Chen L; Zhang X
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37200157
[TBL] [Abstract][Full Text] [Related]
26. Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.
Gan Y; Chen Y; Xu G; Guo W; Zou G
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37313714
[TBL] [Abstract][Full Text] [Related]
27. Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning.
Li B; Peng C; You Z; Zhang X; Zhang S
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37898127
[TBL] [Abstract][Full Text] [Related]
28. Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.
Lei T; Chen R; Zhang S; Chen Y
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37769630
[TBL] [Abstract][Full Text] [Related]
29. An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation.
Yang Q; Xu Z; Zhou W; Wang P; Jiang Q; Juan L
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37225419
[TBL] [Abstract][Full Text] [Related]
30. 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]
31. Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads.
De Rop FV; Ismail JN; Bravo González-Blas C; Hulselmans GJ; Flerin CC; Janssens J; Theunis K; Christiaens VM; Wouters J; Marcassa G; de Wit J; Poovathingal S; Aerts S
Elife; 2022 Feb; 11():. PubMed ID: 35195064
[TBL] [Abstract][Full Text] [Related]
32. The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow.
Mercatelli D; Balboni N; Giorgio F; Aleo E; Garone C; Giorgi FM
Methods Protoc; 2021 May; 4(2):. PubMed ID: 34066513
[TBL] [Abstract][Full Text] [Related]
33. RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest.
Zhao Y; Fang ZY; Lin CX; Deng C; Xu YP; Li HD
Front Genet; 2021; 12():665843. PubMed ID: 34386033
[TBL] [Abstract][Full Text] [Related]
34. InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.
Wang L; Nie R; Miao X; Cai Y; Wang A; Zhang H; Zhang J; Cai J
BMC Bioinformatics; 2024 Jan; 25(1):41. PubMed ID: 38267858
[TBL] [Abstract][Full Text] [Related]
35. Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline.
Mikolajewicz N; Gacesa R; Aguilera-Uribe M; Brown KR; Moffat J; Han H
Commun Biol; 2022 Oct; 5(1):1142. PubMed ID: 36307536
[TBL] [Abstract][Full Text] [Related]
36. A robust semi-supervised NMF model for single cell RNA-seq data.
Wu P; An M; Zou HR; Zhong CY; Wang W; Wu CP
PeerJ; 2020; 8():e10091. PubMed ID: 33088619
[TBL] [Abstract][Full Text] [Related]
37. scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network.
Xu Z; Luo J; Xiong Z
Bioinformatics; 2022 Nov; 38(22):5042-5048. PubMed ID: 36193998
[TBL] [Abstract][Full Text] [Related]
38. A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.
Liu J; Ma J; Wen J; Zhou X
Adv Sci (Weinh); 2024 Jun; ():e2401815. PubMed ID: 38887194
[TBL] [Abstract][Full Text] [Related]
39. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
[TBL] [Abstract][Full Text] [Related]
40. Analysis of single-cell RNA sequencing data based on autoencoders.
Tangherloni A; Ricciuti F; Besozzi D; Liò P; Cvejic A
BMC Bioinformatics; 2021 Jun; 22(1):309. PubMed ID: 34103004
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
