BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

145 related articles for article (PubMed ID: 38373720)

  • 1. MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery.
    Zhang W; Mou M; Hu W; Lu M; Zhang H; Zhang H; Luo Y; Xu H; Tao L; Dai H; Gao J; Zhu F
    J Chem Inf Model; 2024 Apr; 64(7):2720-2732. PubMed ID: 38373720
    [TBL] [Abstract][Full Text] [Related]  

  • 2. DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.
    Lan W; Liao H; Chen Q; Zhu L; Pan Y; Chen YP
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38678587
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification.
    Ouyang D; Liang Y; Li L; Ai N; Lu S; Yu M; Liu X; Xie S
    Comput Biol Med; 2023 Sep; 164():107303. PubMed ID: 37586201
    [TBL] [Abstract][Full Text] [Related]  

  • 4. An integrated Bayesian framework for multi-omics prediction and classification.
    Mallick H; Porwal A; Saha S; Basak P; Svetnik V; Paul E
    Stat Med; 2024 Feb; 43(5):983-1002. PubMed ID: 38146838
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multi-omics integration method based on attention deep learning network for biomedical data classification.
    Gong P; Cheng L; Zhang Z; Meng A; Li E; Chen J; Zhang L
    Comput Methods Programs Biomed; 2023 Apr; 231():107377. PubMed ID: 36739624
    [TBL] [Abstract][Full Text] [Related]  

  • 6. CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.
    Zhao C; Liu A; Zhang X; Cao X; Ding Z; Sha Q; Shen H; Deng HW; Zhou W
    Res Sq; 2023 May; ():. PubMed ID: 37205427
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.
    Zhao C; Liu A; Zhang X; Cao X; Ding Z; Sha Q; Shen H; Deng HW; Zhou W
    Comput Biol Med; 2024 Mar; 170():108058. PubMed ID: 38295477
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.
    Moon S; Lee H
    Bioinformatics; 2022 Apr; 38(8):2287-2296. PubMed ID: 35157023
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Uncertainty-aware dynamic integration for multi-omics classification of tumors.
    Du L; Liu C; Wei R; Chen J
    J Cancer Res Clin Oncol; 2023 Jul; 149(7):3301-3312. PubMed ID: 35925427
    [TBL] [Abstract][Full Text] [Related]  

  • 11. MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.
    Li Y; Guo Z; Gao X; Wang G
    Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38070154
    [TBL] [Abstract][Full Text] [Related]  

  • 12. IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification.
    Hu M; Zhu J; Peng G; Lu W; Wang H; Xie Z
    Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37930027
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets.
    Das S; Srivastava DK
    Genet Epidemiol; 2023 Dec; 47(8):600-616. PubMed ID: 37795815
    [TBL] [Abstract][Full Text] [Related]  

  • 16. moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks.
    Choi JM; Chae H
    BMC Bioinformatics; 2023 Apr; 24(1):169. PubMed ID: 37101124
    [TBL] [Abstract][Full Text] [Related]  

  • 17. GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.
    Jeong D; Koo B; Oh M; Kim TB; Kim S
    Bioinformatics; 2023 Oct; 39(10):. PubMed ID: 37740295
    [TBL] [Abstract][Full Text] [Related]  

  • 18. TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction.
    Luo H; Liang H; Liu H; Fan Z; Wei Y; Yao X; Cong S
    Int J Mol Sci; 2024 Jan; 25(3):. PubMed ID: 38338932
    [TBL] [Abstract][Full Text] [Related]  

  • 19. HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.
    Wang H; Lin K; Zhang Q; Shi J; Song X; Wu J; Zhao C; He K
    Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38530977
    [TBL] [Abstract][Full Text] [Related]  

  • 20. moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.
    Choi JM; Park C; Chae H
    PeerJ; 2024; 12():e17006. PubMed ID: 38426141
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.