148 related articles for article (PubMed ID: 38625746)
1. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.
Jiang Q; Chen S; Chen X; Jiang R
Bioinformatics; 2024 May; 40(5):. PubMed ID: 38625746
[TBL] [Abstract][Full Text] [Related]
2. scGen predicts single-cell perturbation responses.
Lotfollahi M; Wolf FA; Theis FJ
Nat Methods; 2019 Aug; 16(8):715-721. PubMed ID: 31363220
[TBL] [Abstract][Full Text] [Related]
3. AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects.
Bai D; Ellington CN; Mo S; Song L; Xing EP
Bioinformatics; 2024 Jun; 40(Supplement_1):i453-i461. PubMed ID: 38940174
[TBL] [Abstract][Full Text] [Related]
4. ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes.
Hawkins DY; Zuch DT; Huth J; Rodriguez-Sastre N; McCutcheon KR; Glick A; Lion AT; Thomas CF; Descoteaux AE; Johnson WE; Bradham CA
Bioinformatics; 2023 May; 39(5):. PubMed ID: 37086439
[TBL] [Abstract][Full Text] [Related]
5. Predicting single-cell cellular responses to perturbations using cycle consistency learning.
Huang W; Liu H
Bioinformatics; 2024 Jun; 40(Supplement_1):i462-i470. PubMed ID: 38940153
[TBL] [Abstract][Full Text] [Related]
6. Learning single-cell perturbation responses using neural optimal transport.
Bunne C; Stark SG; Gut G; Del Castillo JS; Levesque M; Lehmann KV; Pelkmans L; Krause A; Rätsch G
Nat Methods; 2023 Nov; 20(11):1759-1768. PubMed ID: 37770709
[TBL] [Abstract][Full Text] [Related]
7. scPreGAN, a deep generative model for predicting the response of single-cell expression to perturbation.
Wei X; Dong J; Wang F
Bioinformatics; 2022 Jun; 38(13):3377-3384. PubMed ID: 35639705
[TBL] [Abstract][Full Text] [Related]
8. ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders.
Heydari AA; Davalos OA; Zhao L; Hoyer KK; Sindi SS
Bioinformatics; 2022 Apr; 38(8):2194-2201. PubMed ID: 35179571
[TBL] [Abstract][Full Text] [Related]
9. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.
Chen S; Hua K; Cui H; Jiang R
BMC Bioinformatics; 2019 May; 20(Suppl 7):0. PubMed ID: 31074382
[TBL] [Abstract][Full Text] [Related]
10. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
Song D; Li K; Hemminger Z; Wollman R; Li JJ
Bioinformatics; 2021 Jul; 37(Suppl_1):i358-i366. PubMed ID: 34252925
[TBL] [Abstract][Full Text] [Related]
11. A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data.
Vandenbon A; Diez D
Nat Commun; 2020 Aug; 11(1):4318. PubMed ID: 32859930
[TBL] [Abstract][Full Text] [Related]
12. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.
Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X
Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596
[TBL] [Abstract][Full Text] [Related]
13. EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.
Wei X; Li Z; Ji H; Wu H
Bioinformatics; 2022 May; 38(10):2692-2699. PubMed ID: 35561178
[TBL] [Abstract][Full Text] [Related]
14. SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data.
Yuan M; Wan H; Wang Z; Guo Q; Deng M
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38279647
[TBL] [Abstract][Full Text] [Related]
15. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data.
Sanchez-Castillo M; Blanco D; Tienda-Luna IM; Carrion MC; Huang Y
Bioinformatics; 2018 Mar; 34(6):964-970. PubMed ID: 29028984
[TBL] [Abstract][Full Text] [Related]
16. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.
Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA
Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008
[TBL] [Abstract][Full Text] [Related]
17. SCMarker: Ab initio marker selection for single cell transcriptome profiling.
Wang F; Liang S; Kumar T; Navin N; Chen K
PLoS Comput Biol; 2019 Oct; 15(10):e1007445. PubMed ID: 31658262
[TBL] [Abstract][Full Text] [Related]
18. Model-based understanding of single-cell CRISPR screening.
Duan B; Zhou C; Zhu C; Yu Y; Li G; Zhang S; Zhang C; Ye X; Ma H; Qu S; Zhang Z; Wang P; Sun S; Liu Q
Nat Commun; 2019 May; 10(1):2233. PubMed ID: 31110232
[TBL] [Abstract][Full Text] [Related]
19. TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.
Kim J; T Jakobsen S; Natarajan KN; Won KJ
Nucleic Acids Res; 2021 Jan; 49(1):e1. PubMed ID: 33170214
[TBL] [Abstract][Full Text] [Related]
20. Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge.
Oulas A; Savva K; Karathanasis N; Spyrou GM
PLoS Comput Biol; 2024 Apr; 20(4):e1011550. PubMed ID: 38635836
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
