BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

198 related articles for article (PubMed ID: 36929074)

  • 1. Statistical Methods for Integrative Clustering of Multi-omics Data.
    Chalise P; Kwon D; Fridley BL; Mo Q
    Methods Mol Biol; 2023; 2629():73-93. PubMed ID: 36929074
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.
    Mo Q; Shen R; Guo C; Vannucci M; Chan KS; Hilsenbeck SG
    Biostatistics; 2018 Jan; 19(1):71-86. PubMed ID: 28541380
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.
    Ni Y; He J; Chalise P
    Stat Appl Genet Mol Biol; 2023 Jan; 22(1):. PubMed ID: 37937887
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration.
    Pierre-Jean M; Deleuze JF; Le Floch E; Mauger F
    Brief Bioinform; 2020 Dec; 21(6):2011-2030. PubMed ID: 31792509
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Evaluation of integrative clustering methods for the analysis of multi-omics data.
    Chauvel C; Novoloaca A; Veyre P; Reynier F; Becker J
    Brief Bioinform; 2020 Mar; 21(2):541-552. PubMed ID: 31220206
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization.
    Kim S; Oesterreich S; Kim S; Park Y; Tseng GC
    Biostatistics; 2017 Jan; 18(1):165-179. PubMed ID: 27549122
    [TBL] [Abstract][Full Text] [Related]  

  • 8. scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.
    Qiu Y; Guo D; Zhao P; Zou Q
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38754408
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization.
    Chalise P; Ni Y; Fridley BL
    Comput Biol Med; 2020 Mar; 118():103625. PubMed ID: 31999549
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
    Wu D; Wang D; Zhang MQ; Gu J
    BMC Genomics; 2015 Dec; 16():1022. PubMed ID: 26626453
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering.
    Crippa V; Malighetti F; Villa M; Graudenzi A; Piazza R; Mologni L; Ramazzotti D
    Comput Biol Med; 2023 Aug; 162():107064. PubMed ID: 37267828
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Integrative analysis of multi-omics data for liquid biopsy.
    Chen G; Zhang J; Fu Q; Taly V; Tan F
    Br J Cancer; 2023 Feb; 128(4):505-518. PubMed ID: 36357703
    [TBL] [Abstract][Full Text] [Related]  

  • 13. HCNM: Heterogeneous Correlation Network Model for Multi-level Integrative Study of Multi-omics Data for Cancer Subtype Prediction.
    Vangimalla RR; Sreevalsan-Nair J
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():1880-1886. PubMed ID: 34891654
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma.
    Zhao Y; Gao Y; Xu X; Zhou J; Wang H
    BMC Cancer; 2021 Mar; 21(1):257. PubMed ID: 33750346
    [TBL] [Abstract][Full Text] [Related]  

  • 15. PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data.
    Pierre-Jean M; Mauger F; Deleuze JF; Le Floch E
    Bioinformatics; 2022 Jan; 38(4):900-907. PubMed ID: 34849583
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.
    Wei Z; Zhang Y; Weng W; Chen J; Cai H
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32533167
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Paired single-cell multi-omics data integration with Mowgli.
    Huizing GJ; Deutschmann IM; Peyré G; Cantini L
    Nat Commun; 2023 Nov; 14(1):7711. PubMed ID: 38001063
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.
    Chalise P; Fridley BL
    PLoS One; 2017; 12(5):e0176278. PubMed ID: 28459819
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.