254 related articles for article (PubMed ID: 33303016)
1. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data.
Yuan Y; Bar-Joseph Z
Genome Biol; 2020 Dec; 21(1):300. PubMed ID: 33303016
[TBL] [Abstract][Full Text] [Related]
2. SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.
Si Z; Li H; Shang W; Zhao Y; Kong L; Long C; Zuo Y; Feng Z
Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38811360
[TBL] [Abstract][Full Text] [Related]
3. Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.
Song T; Markham KK; Li Z; Muller KE; Greenham K; Kuang R
Bioinformatics; 2022 Feb; 38(5):1344-1352. PubMed ID: 34864909
[TBL] [Abstract][Full Text] [Related]
4. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks.
Mao G; Pang Z; Zuo K; Wang Q; Pei X; Chen X; Liu J
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37985457
[TBL] [Abstract][Full Text] [Related]
5. Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep.
Liu T; Fang Z; Li X; Zhang L; Cao DS; Li M; Yin M
Bioinformatics; 2024 Jan; 40(1):. PubMed ID: 38243703
[TBL] [Abstract][Full Text] [Related]
6. MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images.
Huang Y; Yu G; Yang Y
PLoS Comput Biol; 2023 Nov; 19(11):e1011623. PubMed ID: 37939200
[TBL] [Abstract][Full Text] [Related]
7. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.
Hu J; Li X; Coleman K; Schroeder A; Ma N; Irwin DJ; Lee EB; Shinohara RT; Li M
Nat Methods; 2021 Nov; 18(11):1342-1351. PubMed ID: 34711970
[TBL] [Abstract][Full Text] [Related]
8. TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics.
Lee Y; Xu Y; Gao P; Chen J
J Mol Biol; 2024 May; 436(9):168543. PubMed ID: 38508302
[TBL] [Abstract][Full Text] [Related]
9. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.
Song Q; Su J
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33480403
[TBL] [Abstract][Full Text] [Related]
10. MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data.
Yang B; Xu Y; Maxwell A; Koh W; Gong P; Zhang C
BMC Syst Biol; 2018 Dec; 12(Suppl 7):115. PubMed ID: 30547796
[TBL] [Abstract][Full Text] [Related]
11. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks.
Shi X; Zhu J; Long Y; Liang C
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37544658
[TBL] [Abstract][Full Text] [Related]
12. HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.
Ma Y; Liu L; Zhao Y; Hang B; Zhang Y
BMC Genomics; 2024 Jun; 25(1):566. PubMed ID: 38840049
[TBL] [Abstract][Full Text] [Related]
13. Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks.
Zeng Y; Wei Z; Yu W; Yin R; Yuan Y; Li B; Tang Z; Lu Y; Yang Y
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35849101
[TBL] [Abstract][Full Text] [Related]
14. ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets.
Fernández Navarro J; Lundeberg J; Ståhl PL
Bioinformatics; 2019 Mar; 35(6):1058-1060. PubMed ID: 30875427
[TBL] [Abstract][Full Text] [Related]
15. Graph deep learning enabled spatial domains identification for spatial transcriptomics.
Liu T; Fang ZY; Li X; Zhang LN; Cao DS; Yin MZ
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37080761
[TBL] [Abstract][Full Text] [Related]
16. Cell clustering for spatial transcriptomics data with graph neural networks.
Li J; Chen S; Pan X; Yuan Y; Shen HB
Nat Comput Sci; 2022 Jun; 2(6):399-408. PubMed ID: 38177586
[TBL] [Abstract][Full Text] [Related]
17. Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
Choi W; Lee H
PLoS One; 2021; 16(10):e0258626. PubMed ID: 34653225
[TBL] [Abstract][Full Text] [Related]
18. Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images.
Zhang J; Hua Z; Yan K; Tian K; Yao J; Liu E; Liu M; Han X
Med Image Anal; 2021 Oct; 73():102183. PubMed ID: 34340108
[TBL] [Abstract][Full Text] [Related]
19. Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases.
Scherer P; Trębacz M; Simidjievski N; Viñas R; Shams Z; Terre HA; Jamnik M; Liò P
Bioinformatics; 2022 Feb; 38(5):1320-1327. PubMed ID: 34888618
[TBL] [Abstract][Full Text] [Related]
20. Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis.
Chen C; Zhang Z; Tang P; Liu X; Huang B
Comput Biol Med; 2024 May; 174():108449. PubMed ID: 38626512
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]