BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 27129736)

  • 1. Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.
    Vazquez AI; Veturi Y; Behring M; Shrestha S; Kirst M; Resende MF; de Los Campos G
    Genetics; 2016 Jul; 203(3):1425-38. PubMed ID: 27129736
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
    Kim D; Li R; Dudek SM; Ritchie MD
    J Biomed Inform; 2015 Aug; 56():220-8. PubMed ID: 26048077
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Whole-Genome Multi-omic Study of Survival in Patients with Glioblastoma Multiforme.
    Bernal Rubio YL; González-Reymúndez A; Wu KH; Griguer CE; Steibel JP; de Los Campos G; Doseff A; Gallo K; Vazquez AI
    G3 (Bethesda); 2018 Nov; 8(11):3627-3636. PubMed ID: 30228192
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Topological integration of RPPA proteomic data with multi-omics data for survival prediction in breast cancer via pathway activity inference.
    Kim TR; Jeong HH; Sohn KA
    BMC Med Genomics; 2019 Jul; 12(Suppl 5):94. PubMed ID: 31296204
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions.
    González-Reymúndez A; de Los Campos G; Gutiérrez L; Lunt SY; Vazquez AI
    Eur J Hum Genet; 2017 May; 25(5):538-544. PubMed ID: 28272536
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An integrated genomics analysis of epigenetic subtypes in human breast tumors links DNA methylation patterns to chromatin states in normal mammary cells.
    Holm K; Staaf J; Lauss M; Aine M; Lindgren D; Bendahl PO; Vallon-Christersson J; Barkardottir RB; Höglund M; Borg Å; Jönsson G; Ringnér M
    Breast Cancer Res; 2016 Feb; 18(1):27. PubMed ID: 26923702
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.
    Kim SY; Jeong HH; Kim J; Moon JH; Sohn KA
    Biol Direct; 2019 Apr; 14(1):8. PubMed ID: 31036036
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Integrating multi-omics for uncovering the architecture of cross-talking pathways in breast cancer.
    Wang L; Xiao Y; Ping Y; Li J; Zhao H; Li F; Hu J; Zhang H; Deng Y; Tian J; Li X
    PLoS One; 2014; 9(8):e104282. PubMed ID: 25137136
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.
    Tong L; Wu H; Wang MD
    Methods; 2021 May; 189():74-85. PubMed ID: 32763377
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.
    Lin Y; Zhang W; Cao H; Li G; Du W
    Genes (Basel); 2020 Aug; 11(8):. PubMed ID: 32759821
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data.
    Baek B; Lee H
    Sci Rep; 2020 Nov; 10(1):18951. PubMed ID: 33144687
    [TBL] [Abstract][Full Text] [Related]  

  • 12. eBreCaP: extreme learning-based model for breast cancer survival prediction.
    Dhillon A; Singh A
    IET Syst Biol; 2020 Jun; 14(3):160-169. PubMed ID: 32406380
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis.
    Tong L; Mitchel J; Chatlin K; Wang MD
    BMC Med Inform Decis Mak; 2020 Sep; 20(1):225. PubMed ID: 32933515
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
    Liu C; Wang X; Genchev GZ; Lu H
    Methods; 2017 Jul; 124():100-107. PubMed ID: 28627406
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.
    Chung RH; Kang CY
    Gigascience; 2019 May; 8(5):. PubMed ID: 31029063
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
    Suo C; Hrydziuszko O; Lee D; Pramana S; Saputra D; Joshi H; Calza S; Pawitan Y
    Bioinformatics; 2015 Aug; 31(16):2607-13. PubMed ID: 25810432
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.
    Pineda S; Real FX; Kogevinas M; Carrato A; Chanock SJ; Malats N; Van Steen K
    PLoS Genet; 2015 Dec; 11(12):e1005689. PubMed ID: 26646822
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in
    Hansen PB; Ruud AK; de Los Campos G; Malinowska M; Nagy I; Svane SF; Thorup-Kristensen K; Jensen JD; Krusell L; Asp T
    Plants (Basel); 2022 Aug; 11(17):. PubMed ID: 36079572
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study.
    Li Y; Mansmann U; Du S; Hornung R
    Genes (Basel); 2021 Nov; 12(12):. PubMed ID: 34946821
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Methylation-to-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, and Pathologic Complete Response in Multiple Cohorts.
    Thompson JA; Christensen BC; Marsit CJ
    Sci Rep; 2018 Mar; 8(1):5190. PubMed ID: 29581450
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.