130 related articles for article (PubMed ID: 37527304)
1. An Integrated Method Based on Wasserstein Distance and Graph for Cancer Subtype Discovery.
Cao Q; Zhao J; Wang H; Guan Q; Zheng C
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3499-3510. PubMed ID: 37527304
[TBL] [Abstract][Full Text] [Related]
2. Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping.
Zhu S; Wang W; Fang W; Cui M
Math Biosci Eng; 2023 Nov; 20(12):21098-21119. PubMed ID: 38124589
[TBL] [Abstract][Full Text] [Related]
3. MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.
Rong Z; Liu Z; Song J; Cao L; Yu Y; Qiu M; Hou Y
Comput Biol Med; 2022 Nov; 150():106085. PubMed ID: 36162197
[TBL] [Abstract][Full Text] [Related]
4. MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism.
Zhang G; Peng Z; Yan C; Wang J; Luo J; Luo H
Front Genet; 2022; 13():855629. PubMed ID: 35391797
[TBL] [Abstract][Full Text] [Related]
5. Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance.
Zhu H; Hao H; Yu L
BMC Biol; 2023 Dec; 21(1):294. PubMed ID: 38115088
[TBL] [Abstract][Full Text] [Related]
6. stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics.
Fang Z; Liu T; Zheng R; A J; Yin M; Li M
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38189544
[TBL] [Abstract][Full Text] [Related]
7. Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis.
Zhang Y; Xiong S; Wang Z; Liu Y; Luo H; Li B; Zou Q
Methods; 2023 May; 213():1-9. PubMed ID: 36933628
[TBL] [Abstract][Full Text] [Related]
8. Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data.
Zhao J; Zhao B; Song X; Lyu C; Chen W; Xiong Y; Wei DQ
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36702755
[TBL] [Abstract][Full Text] [Related]
9. Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping.
Madhumita ; Paul S
Comput Biol Med; 2022 Sep; 148():105832. PubMed ID: 35834966
[TBL] [Abstract][Full Text] [Related]
10. Cancer subtype identification by consensus guided graph autoencoders.
Liang C; Shang M; Luo J
Bioinformatics; 2021 Dec; 37(24):4779-4786. PubMed ID: 34289034
[TBL] [Abstract][Full Text] [Related]
11. Integrated multi-omics analysis of ovarian cancer using variational autoencoders.
Hira MT; Razzaque MA; Angione C; Scrivens J; Sawan S; Sarker M
Sci Rep; 2021 Mar; 11(1):6265. PubMed ID: 33737557
[TBL] [Abstract][Full Text] [Related]
12. MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis.
Li X; Ma J; Leng L; Han M; Li M; He F; Zhu Y
Front Genet; 2022; 13():806842. PubMed ID: 35186034
[TBL] [Abstract][Full Text] [Related]
13. Similarity Fusion via Exploiting High Order Proximity for Cancer Subtyping.
Chen J; Rong W; Tao G; Cai H
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):658-667. PubMed ID: 34971537
[TBL] [Abstract][Full Text] [Related]
14. Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction.
Liu J; Ge S; Cheng Y; Wang X
Front Genet; 2021; 12():718915. PubMed ID: 34552619
[TBL] [Abstract][Full Text] [Related]
15. Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data.
Liu C; Cao W; Wu S; Shen W; Jiang D; Yu Z; Wong HS
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):1193-1202. PubMed ID: 32750893
[TBL] [Abstract][Full Text] [Related]
16. Multi-View Random-Walk Graph Regularization Low-Rank Representation for Cancer Clustering and Differentially Expressed Gene Selection.
Wang J; Wang LH; Liu JX; Kong XZ; Li SJ
IEEE J Biomed Health Inform; 2022 Jul; 26(7):3578-3589. PubMed ID: 35157604
[TBL] [Abstract][Full Text] [Related]
17. A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data.
Xu J; Wu P; Chen Y; Meng Q; Dawood H; Dawood H
BMC Bioinformatics; 2019 Oct; 20(1):527. PubMed ID: 31660856
[TBL] [Abstract][Full Text] [Related]
18. Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data.
Shetta O; Niranjan M; Dasmahapatra S
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(6):3340-3352. PubMed ID: 34705655
[TBL] [Abstract][Full Text] [Related]
19. Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model.
Song H; Ruan C; Xu Y; Xu T; Fan R; Jiang T; Cao M; Song J
Exp Biol Med (Maywood); 2022 Jun; 247(11):898-909. PubMed ID: 34904882
[TBL] [Abstract][Full Text] [Related]
20. PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.
Lemsara A; Ouadfel S; Fröhlich H
BMC Bioinformatics; 2020 Apr; 21(1):146. PubMed ID: 32299344
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
