BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

147 related articles for article (PubMed ID: 28968753)

  • 1. A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network.
    Zhong Y; Xuan P; Wang X; Zhang T; Li J; Liu Y; Zhang W
    Bioinformatics; 2018 Jan; 34(2):267-277. PubMed ID: 28968753
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of potential disease-associated microRNAs based on random walk.
    Xuan P; Han K; Guo Y; Li J; Li X; Zhong Y; Zhang Z; Ding J
    Bioinformatics; 2015 Jun; 31(11):1805-15. PubMed ID: 25618864
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Inferring disease-associated microRNAs in heterogeneous networks with node attributes.
    Xuan P; Shen T; Wang X; Zhang T; Zhang W
    IEEE/ACM Trans Comput Biol Bioinform; 2018 Sep; ():. PubMed ID: 30281474
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges.
    Xuan P; Li L; Zhang T; Zhang Y; Song Y
    Molecules; 2019 Aug; 24(17):. PubMed ID: 31455026
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.
    Xiao Q; Luo J; Liang C; Cai J; Ding P
    Bioinformatics; 2018 Jan; 34(2):239-248. PubMed ID: 28968779
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.
    Xuan P; Sun H; Wang X; Zhang T; Pan S
    Int J Mol Sci; 2019 Jul; 20(15):. PubMed ID: 31349729
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information.
    Xuan P; Zhang Y; Zhang T; Li L; Zhao L
    Genes (Basel); 2019 Sep; 10(9):. PubMed ID: 31500152
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of potential disease-associated microRNAs using structural perturbation method.
    Zeng X; Liu L; Lü L; Zou Q
    Bioinformatics; 2018 Jul; 34(14):2425-2432. PubMed ID: 29490018
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DNRLMF-MDA:Predicting microRNA-Disease Associations Based on Similarities of microRNAs and Diseases.
    Yan C; Wang J; Ni P; Lan W; Wu FX; Pan Y
    IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):233-243. PubMed ID: 29990253
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square.
    Zhao Y; Chen X; Yin J
    Front Genet; 2018; 9():324. PubMed ID: 30186308
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting miRNA-disease association based on inductive matrix completion.
    Chen X; Wang L; Qu J; Guan NN; Li JQ
    Bioinformatics; 2018 Dec; 34(24):4256-4265. PubMed ID: 29939227
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
    Xuan P; Han K; Guo M; Guo Y; Li J; Ding J; Liu Y; Dai Q; Li J; Teng Z; Huang Y
    PLoS One; 2013; 8(8):e70204. PubMed ID: 23950912
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction.
    Li J; Zhang S; Liu T; Ning C; Zhang Z; Zhou W
    Bioinformatics; 2020 Apr; 36(8):2538-2546. PubMed ID: 31904845
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks.
    Zeng X; Wang W; Deng G; Bing J; Zou Q
    Mol Ther Nucleic Acids; 2019 Jun; 16():566-575. PubMed ID: 31077936
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.
    Xuan P; Dong Y; Guo Y; Zhang T; Liu Y
    Int J Mol Sci; 2018 Nov; 19(12):. PubMed ID: 30477152
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network.
    Luo J; Xiao Q
    J Biomed Inform; 2017 Feb; 66():194-203. PubMed ID: 28104458
    [TBL] [Abstract][Full Text] [Related]  

  • 19. PRMDA: personalized recommendation-based MiRNA-disease association prediction.
    You ZH; Wang LP; Chen X; Zhang S; Li XF; Yan GY; Li ZW
    Oncotarget; 2017 Oct; 8(49):85568-85583. PubMed ID: 29156742
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Meta-Path Methods for Prioritizing Candidate Disease miRNAs.
    Zhang X; Zou Q; Rodriguez-Paton A; Zeng X
    IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):283-291. PubMed ID: 29990255
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.