BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

414 related articles for article (PubMed ID: 30537965)

  • 1. A heterogeneous label propagation approach to explore the potential associations between miRNA and disease.
    Chen X; Zhang DH; You ZH
    J Transl Med; 2018 Dec; 16(1):348. PubMed ID: 30537965
    [TBL] [Abstract][Full Text] [Related]  

  • 2. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.
    Chen X; Yin J; Qu J; Huang L
    PLoS Comput Biol; 2018 Aug; 14(8):e1006418. PubMed ID: 30142158
    [TBL] [Abstract][Full Text] [Related]  

  • 3. MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.
    Chen X; Niu YW; Wang GH; Yan GY
    J Transl Med; 2017 Dec; 15(1):251. PubMed ID: 29233191
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction.
    Chen X; Jiang ZC; Xie D; Huang DS; Zhao Q; Yan GY; You ZH
    Mol Biosyst; 2017 May; 13(6):1202-1212. PubMed ID: 28470244
    [TBL] [Abstract][Full Text] [Related]  

  • 5. An improved random forest-based computational model for predicting novel miRNA-disease associations.
    Yao D; Zhan X; Kwoh CK
    BMC Bioinformatics; 2019 Dec; 20(1):624. PubMed ID: 31795954
    [TBL] [Abstract][Full Text] [Related]  

  • 6. WBNPMD: weighted bipartite network projection for microRNA-disease association prediction.
    Xie G; Fan Z; Sun Y; Wu C; Ma L
    J Transl Med; 2019 Sep; 17(1):322. PubMed ID: 31547811
    [TBL] [Abstract][Full Text] [Related]  

  • 7. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
    You ZH; Huang ZA; Zhu Z; Yan GY; Li ZW; Wen Z; Chen X
    PLoS Comput Biol; 2017 Mar; 13(3):e1005455. PubMed ID: 28339468
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of potential disease-associated microRNAs by composite network based inference.
    He BS; Qu J; Chen M
    Sci Rep; 2018 Oct; 8(1):15813. PubMed ID: 30361693
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MicroRNAs and complex diseases: from experimental results to computational models.
    Chen X; Xie D; Zhao Q; You ZH
    Brief Bioinform; 2019 Mar; 20(2):515-539. PubMed ID: 29045685
    [TBL] [Abstract][Full Text] [Related]  

  • 10. WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.
    Chen X; Yan CC; Zhang X; You ZH; Deng L; Liu Y; Zhang Y; Dai Q
    Sci Rep; 2016 Feb; 6():21106. PubMed ID: 26880032
    [TBL] [Abstract][Full Text] [Related]  

  • 11. NDAMDA: Network distance analysis for MiRNA-disease association prediction.
    Chen X; Wang LY; Huang L
    J Cell Mol Med; 2018 May; 22(5):2884-2895. PubMed ID: 29532987
    [TBL] [Abstract][Full Text] [Related]  

  • 12. DRMDA: deep representations-based miRNA-disease association prediction.
    Chen X; Gong Y; Zhang DH; You ZH; Li ZW
    J Cell Mol Med; 2018 Jan; 22(1):472-485. PubMed ID: 28857494
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.
    Zhang L; Liu B; Li Z; Zhu X; Liang Z; An J
    BMC Bioinformatics; 2020 Oct; 21(1):470. PubMed ID: 33087064
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of microRNA-disease associations based on distance correlation set.
    Zhao H; Kuang L; Wang L; Ping P; Xuan Z; Pei T; Wu Z
    BMC Bioinformatics; 2018 Apr; 19(1):141. PubMed ID: 29665774
    [TBL] [Abstract][Full Text] [Related]  

  • 15. EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction.
    Chen X; Huang L; Xie D; Zhao Q
    Cell Death Dis; 2018 Jan; 9(1):3. PubMed ID: 29305594
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Maximal entropy random walk on heterogenous network for MIRNA-disease Association prediction.
    Niu YW; Liu H; Wang GH; Yan GY
    Math Biosci; 2018 Dec; 306():1-9. PubMed ID: 30336146
    [TBL] [Abstract][Full Text] [Related]  

  • 17. In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.
    Guan NN; Wang CC; Zhang L; Huang L; Li JQ; Piao X
    J Cell Mol Med; 2020 Jan; 24(1):573-587. PubMed ID: 31747722
    [TBL] [Abstract][Full Text] [Related]  

  • 18. RBMMMDA: predicting multiple types of disease-microRNA associations.
    Chen X; Yan CC; Zhang X; Li Z; Deng L; Zhang Y; Dai Q
    Sci Rep; 2015 Sep; 5():13877. PubMed ID: 26347258
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MCMDA: Matrix completion for MiRNA-disease association prediction.
    Li JQ; Rong ZH; Chen X; Yan GY; You ZH
    Oncotarget; 2017 Mar; 8(13):21187-21199. PubMed ID: 28177900
    [TBL] [Abstract][Full Text] [Related]  

  • 20. RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.
    Chen X; Wu QF; Yan GY
    RNA Biol; 2017 Jul; 14(7):952-962. PubMed ID: 28421868
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.