BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

254 related articles for article (PubMed ID: 27926382)

  • 1. Prediction of anti-cancer drug response by kernelized multi-task learning.
    Tan M
    Artif Intell Med; 2016 Oct; 73():70-77. PubMed ID: 27926382
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Drug response prediction by ensemble learning and drug-induced gene expression signatures.
    Tan M; Özgül OF; Bardak B; Ekşioğlu I; Sabuncuoğlu S
    Genomics; 2019 Sep; 111(5):1078-1088. PubMed ID: 31533900
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.
    Wang L; Li X; Zhang L; Gao Q
    BMC Cancer; 2017 Aug; 17(1):513. PubMed ID: 28768489
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Ensembled machine learning framework for drug sensitivity prediction.
    Sharma A; Rani R
    IET Syst Biol; 2020 Feb; 14(1):39-46. PubMed ID: 31931480
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning.
    Gönen M; Margolin AA
    Bioinformatics; 2014 Sep; 30(17):i556-63. PubMed ID: 25161247
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.
    Huang C; Mezencev R; McDonald JF; Vannberg F
    PLoS One; 2017; 12(10):e0186906. PubMed ID: 29073279
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization.
    Ammad-Ud-Din M; Khan SA; Malani D; Murumägi A; Kallioniemi O; Aittokallio T; Kaski S
    Bioinformatics; 2016 Sep; 32(17):i455-i463. PubMed ID: 27587662
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction.
    Turki T; Wei Z; Wang JTL
    J Bioinform Comput Biol; 2018 Jun; 16(3):1840014. PubMed ID: 29945499
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Drug Response Prediction by Globally Capturing Drug and Cell Line Information in a Heterogeneous Network.
    Le DH; Pham VH
    J Mol Biol; 2018 Sep; 430(18 Pt A):2993-3004. PubMed ID: 29966608
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Kernelized rank learning for personalized drug recommendation.
    He X; Folkman L; Borgwardt K
    Bioinformatics; 2018 Aug; 34(16):2808-2816. PubMed ID: 29528376
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting drug response of tumors from integrated genomic profiles by deep neural networks.
    Chiu YC; Chen HH; Zhang T; Zhang S; Gorthi A; Wang LJ; Huang Y; Chen Y
    BMC Med Genomics; 2019 Jan; 12(Suppl 1):18. PubMed ID: 30704458
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Computational identification of multi-omic correlates of anticancer therapeutic response.
    Stetson LC; Pearl T; Chen Y; Barnholtz-Sloan JS
    BMC Genomics; 2014; 15 Suppl 7(Suppl 7):S2. PubMed ID: 25573145
    [TBL] [Abstract][Full Text] [Related]  

  • 13. In silico drug combination discovery for personalized cancer therapy.
    Jeon M; Kim S; Park S; Lee H; Kang J
    BMC Syst Biol; 2018 Mar; 12(Suppl 2):16. PubMed ID: 29560824
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Clinical intelligence: New machine learning techniques for predicting clinical drug response.
    Turki T; Wang JTL
    Comput Biol Med; 2019 Apr; 107():302-322. PubMed ID: 30771879
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.
    Zhang X; Jang MI; Zheng Z; Gao A; Lin Z; Kim KY
    Anticancer Res; 2021 May; 41(5):2419-2429. PubMed ID: 33952467
    [TBL] [Abstract][Full Text] [Related]  

  • 16. GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction.
    Liu X; Song C; Huang F; Fu H; Xiao W; Zhang W
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34727569
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.
    Emdadi A; Eslahchi C
    BMC Bioinformatics; 2021 Jan; 22(1):33. PubMed ID: 33509079
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Identifying anti-cancer drug response related genes using an integrative analysis of transcriptomic and genomic variations with cell line-based drug perturbations.
    Sun Y; Zhang W; Chen Y; Ma Q; Wei J; Liu Q
    Oncotarget; 2016 Feb; 7(8):9404-19. PubMed ID: 26824188
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.
    Borisov N; Tkachev V; Suntsova M; Kovalchuk O; Zhavoronkov A; Muchnik I; Buzdin A
    Cell Cycle; 2018; 17(4):486-491. PubMed ID: 29251172
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.