These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

222 related articles for article (PubMed ID: 38426322)

  • 1. 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]  

  • 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. Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping.
    Li Z; Katz S; Saccenti E; Fardo DW; Claes P; Martins Dos Santos VAP; Van Steen K; Roshchupkin GV
    Brief Bioinform; 2024 Sep; 25(6):. PubMed ID: 39413796
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation.
    Xie M; Kuang Y; Song M; Bao E
    Bioinformatics; 2024 Jun; 40(6):. PubMed ID: 38857453
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A Multiview Clustering Method With Low-Rank and Sparsity Constraints for Cancer Subtyping.
    Zhanpeng H; Jiekang W
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(6):3213-3223. PubMed ID: 34705654
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 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. Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.
    Wei Y; Li L; Zhao X; Yang H; Sa J; Cao H; Cui Y
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36433785
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep structure integrative representation of multi-omics data for cancer subtyping.
    Yang B; Yang Y; Su X
    Bioinformatics; 2022 Jun; 38(13):3337-3342. PubMed ID: 35639657
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction.
    Ranjbari S; Arslanturk S
    J Biomed Inform; 2023 Nov; 147():104512. PubMed ID: 37813325
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks.
    Wang J; Liao N; Du X; Chen Q; Wei B
    BMC Genomics; 2024 Jan; 25(1):86. PubMed ID: 38254021
    [TBL] [Abstract][Full Text] [Related]  

  • 18. MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.
    Zhong Y; Peng Y; Lin Y; Chen D; Zhang H; Zheng W; Chen Y; Wu C
    BMC Med Inform Decis Mak; 2023 May; 23(1):82. PubMed ID: 37147619
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.
    Hauptmann T; Kramer S
    BMC Bioinformatics; 2023 Feb; 24(1):45. PubMed ID: 36788531
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multi-view contrastive clustering for cancer subtyping using fully and weakly paired multi-omics data.
    Kuang Y; Xie M; Zhao Z; Deng D; Bao E
    Methods; 2024 Dec; 232():1-8. PubMed ID: 39423914
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.