BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

141 related articles for article (PubMed ID: 30304378)

  • 1. A novel approach for drug response prediction in cancer cell lines via network representation learning.
    Yang J; Li A; Li Y; Guo X; Wang M
    Bioinformatics; 2019 May; 35(9):1527-1535. PubMed ID: 30304378
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A novel heterogeneous network-based method for drug response prediction in cancer cell lines.
    Zhang F; Wang M; Xi J; Yang J; Li A
    Sci Rep; 2018 Feb; 8(1):3355. PubMed ID: 29463808
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions.
    Peng W; Liu H; Dai W; Yu N; Wang J
    Bioinformatics; 2022 Sep; 38(19):4546-4553. PubMed ID: 35997568
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction.
    Liu X; Song C; Liu S; Li M; Zhou X; Zhang W
    Bioinformatics; 2022 Oct; 38(20):4782-4789. PubMed ID: 36000898
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach.
    Emdadi A; Eslahchi C
    J Bioinform Comput Biol; 2022 Apr; 20(2):2150035. PubMed ID: 34923927
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting Cancer Drug Response using a Recommender System.
    Suphavilai C; Bertrand D; Nagarajan N
    Bioinformatics; 2018 Nov; 34(22):3907-3914. PubMed ID: 29868820
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.
    Zong N; Kim H; Ngo V; Harismendy O
    Bioinformatics; 2017 Aug; 33(15):2337-2344. PubMed ID: 28430977
    [TBL] [Abstract][Full Text] [Related]  

  • 10. MSDRP: a deep learning model based on multisource data for predicting drug response.
    Zhao H; Zhang X; Zhao Q; Li Y; Wang J
    Bioinformatics; 2023 Sep; 39(9):. PubMed ID: 37606993
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Computational probing protein-protein interactions targeting small molecules.
    Wang YC; Chen SL; Deng NY; Wang Y
    Bioinformatics; 2016 Jan; 32(2):226-34. PubMed ID: 26415726
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation.
    Zhu Y; Ouyang Z; Chen W; Feng R; Chen DZ; Cao J; Wu J
    Bioinformatics; 2022 Jan; 38(2):461-468. PubMed ID: 34559177
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Network Propagation Predicts Drug Synergy in Cancers.
    Li H; Li T; Quang D; Guan Y
    Cancer Res; 2018 Sep; 78(18):5446-5457. PubMed ID: 30054332
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Drug response prediction using graph representation learning and Laplacian feature selection.
    Xie M; Lei X; Zhong J; Ouyang J; Li G
    BMC Bioinformatics; 2022 Dec; 23(Suppl 8):532. PubMed ID: 36494630
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep learning and multi-omics approach to predict drug responses in cancer.
    Wang C; Lye X; Kaalia R; Kumar P; Rajapakse JC
    BMC Bioinformatics; 2022 Nov; 22(Suppl 10):632. PubMed ID: 36443676
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.
    Emdadi A; Eslahchi C
    Front Genet; 2020; 11():75. PubMed ID: 32174963
    [TBL] [Abstract][Full Text] [Related]  

  • 20. GDSCTools for mining pharmacogenomic interactions in cancer.
    Cokelaer T; Chen E; Iorio F; Menden MP; Lightfoot H; Saez-Rodriguez J; Garnett MJ
    Bioinformatics; 2018 Apr; 34(7):1226-1228. PubMed ID: 29186349
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.