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 *

258 related articles for article (PubMed ID: 25629319)

  • 1. Sparse overlapping group lasso for integrative multi-omics analysis.
    Park H; Niida A; Miyano S; Imoto S
    J Comput Biol; 2015 Feb; 22(2):73-84. PubMed ID: 25629319
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level.
    Park H; Niida A; Imoto S; Miyano S
    J Comput Biol; 2017 Feb; 24(2):138-152. PubMed ID: 27759426
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.
    Ping Y; Deng Y; Wang L; Zhang H; Zhang Y; Xu C; Zhao H; Fan H; Yu F; Xiao Y; Li X
    Nucleic Acids Res; 2015 Feb; 43(4):1997-2007. PubMed ID: 25653168
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods.
    Qin J; Hu Y; Xu F; Yalamanchili HK; Wang J
    Methods; 2014 Jun; 67(3):294-303. PubMed ID: 24650566
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis.
    Li A; Chapuy B; Varelas X; Sebastiani P; Monti S
    Sci Rep; 2019 Nov; 9(1):16904. PubMed ID: 31729402
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism.
    Peng W; Wu R; Dai W; Yu N
    BMC Bioinformatics; 2023 Jan; 24(1):16. PubMed ID: 36639646
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for glioblastoma.
    Gu Y; Wang H; Qin Y; Zhang Y; Zhao W; Qi L; Zhang Y; Wang C; Guo Z
    Mol Biosyst; 2013 Mar; 9(3):467-77. PubMed ID: 23344900
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.
    Zhang SW; Xu JY; Zhang T
    Genomics Proteomics Bioinformatics; 2022 Oct; 20(5):928-938. PubMed ID: 36464123
    [TBL] [Abstract][Full Text] [Related]  

  • 10. IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.
    Shi P; Han J; Zhang Y; Li G; Zhou X
    PLoS Comput Biol; 2024 Aug; 20(8):e1012389. PubMed ID: 39186807
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets.
    Park H; Imoto S; Miyano S
    PLoS One; 2015; 10(11):e0141869. PubMed ID: 26544691
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.
    Liu Q; Muglia LJ; Huang LF
    Genes (Basel); 2019 Aug; 10(8):. PubMed ID: 31405013
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Novel Adaptive Penalized Logistic Regression for Uncovering Biomarker Associated with Anti-Cancer Drug Sensitivity.
    Park H; Shiraishi Y; Imoto S; Miyano S
    IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(4):771-782. PubMed ID: 27164605
    [TBL] [Abstract][Full Text] [Related]  

  • 14. ICan: an integrated co-alteration network to identify ovarian cancer-related genes.
    Zhou Y; Liu Y; Li K; Zhang R; Qiu F; Zhao N; Xu Y
    PLoS One; 2015; 10(3):e0116095. PubMed ID: 25803614
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Integrating omics data and protein interaction networks to prioritize driver genes in cancer.
    Zhang T; Zhang D
    Oncotarget; 2017 Aug; 8(35):58050-58060. PubMed ID: 28938536
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Integration of multi-omics data to mine cancer-related gene modules.
    Li P; Guo M; Sun B
    J Bioinform Comput Biol; 2019 Dec; 17(6):1950038. PubMed ID: 32019413
    [TBL] [Abstract][Full Text] [Related]  

  • 17. DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method.
    Amgalan B; Lee H
    Bioinformatics; 2015 Aug; 31(15):2452-60. PubMed ID: 25819079
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.
    Jeong HH; Leem S; Wee K; Sohn KA
    J Ovarian Res; 2015 Jul; 8():42. PubMed ID: 26138921
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. A novel network control model for identifying personalized driver genes in cancer.
    Guo WF; Zhang SW; Zeng T; Li Y; Gao J; Chen L
    PLoS Comput Biol; 2019 Nov; 15(11):e1007520. PubMed ID: 31765387
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.