542 related articles for article (PubMed ID: 35834966)
1. 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]
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. DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.
Munquad S; Das AB
BioData Min; 2023 Nov; 16(1):32. PubMed ID: 37968655
[TBL] [Abstract][Full Text] [Related]
4. A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.
Li W; Huang Q; Peng Y; Pan S; Hu M; Wang P; He Y
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15923-15938. PubMed ID: 37673824
[TBL] [Abstract][Full Text] [Related]
5. Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data.
Franco EF; Rana P; Cruz A; Calderón VV; Azevedo V; Ramos RTJ; Ghosh P
Cancers (Basel); 2021 Apr; 13(9):. PubMed ID: 33921978
[TBL] [Abstract][Full Text] [Related]
6. Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.
Cai Y; Wang S
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38426322
[TBL] [Abstract][Full Text] [Related]
7. Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data.
Song W; Wang W; Dai DQ
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34607358
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data.
Yang H; Chen R; Li D; Wang Z
Bioinformatics; 2021 Aug; 37(16):2231-2237. PubMed ID: 33599254
[TBL] [Abstract][Full Text] [Related]
10. AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification.
Li M; Guo H; Wang K; Kang C; Yin Y; Zhang H
Comput Biol Med; 2024 Jul; 177():108614. PubMed ID: 38796884
[TBL] [Abstract][Full Text] [Related]
11. Deep learning-based ovarian cancer subtypes identification using multi-omics data.
Guo LY; Wu AH; Wang YX; Zhang LP; Chai H; Liang XF
BioData Min; 2020; 13():10. PubMed ID: 32863885
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Deep latent space fusion for adaptive representation of heterogeneous multi-omics data.
Zhang C; Chen Y; Zeng T; Zhang C; Chen L
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35079777
[TBL] [Abstract][Full Text] [Related]
14. Multi-omics clustering for cancer subtyping based on latent subspace learning.
Ye X; Shang Y; Shi T; Zhang W; Sakurai T
Comput Biol Med; 2023 Sep; 164():107223. PubMed ID: 37490833
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping.
Ge S; Liu J; Cheng Y; Meng X; Wang X
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36445207
[TBL] [Abstract][Full Text] [Related]
17. Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery.
Bansal B; Sahoo A
Comput Methods Programs Biomed; 2023 Jan; 228():107246. PubMed ID: 36434961
[TBL] [Abstract][Full Text] [Related]
18. Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes.
Ji Y; Dutta P; Davuluri R
Bioinform Adv; 2023; 3(1):vbad075. PubMed ID: 37424943
[TBL] [Abstract][Full Text] [Related]
19. Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.
Chen F; Peng W; Dai W; Wei S; Fu X; Liu L; Liu L
Health Inf Sci Syst; 2024 Dec; 12(1):12. PubMed ID: 38404715
[TBL] [Abstract][Full Text] [Related]
20. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.
Malik V; Kalakoti Y; Sundar D
BMC Genomics; 2021 Mar; 22(1):214. PubMed ID: 33761889
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
