BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

152 related articles for article (PubMed ID: 30032047)

  • 1. The L
    Jiang HK; Liang Y
    Comput Biol Med; 2018 Sep; 100():203-208. PubMed ID: 30032047
    [TBL] [Abstract][Full Text] [Related]  

  • 2. NETWORK-REGULARIZED HIGH-DIMENSIONAL COX REGRESSION FOR ANALYSIS OF GENOMIC DATA.
    Sun H; Lin W; Feng R; Li H
    Stat Sin; 2014 Jul; 24(3):1433-1459. PubMed ID: 26316678
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Hybrid L
    Huang HH; Liang Y
    Comput Methods Programs Biomed; 2018 Oct; 164():65-73. PubMed ID: 30195432
    [TBL] [Abstract][Full Text] [Related]  

  • 4. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.
    Zou M; Liu Z; Zhang XS; Wang Y
    Bioinformatics; 2015 Oct; 31(20):3330-8. PubMed ID: 26092859
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.
    Liang Y; Chai H; Liu XY; Xu ZB; Zhang H; Leung KS
    BMC Med Genomics; 2016 Mar; 9():11. PubMed ID: 26932592
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Novel harmonic regularization approach for variable selection in Cox's proportional hazards model.
    Chu GJ; Liang Y; Wang JX
    Comput Math Methods Med; 2014; 2014():857398. PubMed ID: 25506389
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Network-constrained regularization and variable selection for analysis of genomic data.
    Li C; Li H
    Bioinformatics; 2008 May; 24(9):1175-82. PubMed ID: 18310618
    [TBL] [Abstract][Full Text] [Related]  

  • 8. L1 penalized estimation in the Cox proportional hazards model.
    Goeman JJ
    Biom J; 2010 Feb; 52(1):70-84. PubMed ID: 19937997
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification.
    Liang Y; Liu C; Luan XZ; Leung KS; Chan TM; Xu ZB; Zhang H
    BMC Bioinformatics; 2013 Jun; 14():198. PubMed ID: 23777239
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The L(1/2) regularization approach for survival analysis in the accelerated failure time model.
    Chai H; Liang Y; Liu XY
    Comput Biol Med; 2015 Sep; 64():283-90. PubMed ID: 25262114
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.
    Gui J; Li H
    Bioinformatics; 2005 Jul; 21(13):3001-8. PubMed ID: 15814556
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Partial least squares Cox regression for genome-wide data.
    Nygård S; Borgan O; Lingjaerde OC; Størvold HL
    Lifetime Data Anal; 2008 Jun; 14(2):179-95. PubMed ID: 18188699
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Penalized logistic regression based on L1/2 penalty for high-dimensional DNA methylation data.
    Jiang HK; Liang Y
    Technol Health Care; 2020; 28(S1):161-171. PubMed ID: 32364148
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data.
    Kim K; Sun H
    BMC Bioinformatics; 2019 Oct; 20(1):510. PubMed ID: 31640538
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome.
    Sun D; Li A; Tang B; Wang M
    Comput Methods Programs Biomed; 2018 Jul; 161():45-53. PubMed ID: 29852967
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Covariate dimension reduction for survival data via the Gaussian process latent variable model.
    Barrett JE; Coolen AC
    Stat Med; 2016 Apr; 35(8):1340-53. PubMed ID: 26526057
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.
    Xu H; Moni MA; Liò P
    Comput Biol Chem; 2015 Dec; 59 Pt B():15-31. PubMed ID: 26611766
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models.
    Belhechmi S; Bin R; Rotolo F; Michiels S
    BMC Bioinformatics; 2020 Jul; 21(1):277. PubMed ID: 32615919
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.