BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

447 related articles for article (PubMed ID: 29619882)

  • 41. MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.
    Wang YT; Wu QW; Gao Z; Ni JC; Zheng CH
    BMC Med Inform Decis Mak; 2021 Apr; 21(Suppl 1):133. PubMed ID: 33882934
    [TBL] [Abstract][Full Text] [Related]  

  • 42. GRMDA: Graph Regression for MiRNA-Disease Association Prediction.
    Chen X; Yang JR; Guan NN; Li JQ
    Front Physiol; 2018; 9():92. PubMed ID: 29515453
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.
    Yao HB; Hou ZJ; Zhang WG; Li H; Chen Y
    J Comput Biol; 2024 Mar; 31(3):241-256. PubMed ID: 38377572
    [TBL] [Abstract][Full Text] [Related]  

  • 44. RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations.
    Wang CC; Chen X; Qu J; Sun YZ; Li JQ
    J Chem Inf Model; 2019 Apr; 59(4):1668-1679. PubMed ID: 30840454
    [TBL] [Abstract][Full Text] [Related]  

  • 45. MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.
    Zheng K; You ZH; Wang L; Zhou Y; Li LP; Li ZW
    J Transl Med; 2019 Aug; 17(1):260. PubMed ID: 31395072
    [TBL] [Abstract][Full Text] [Related]  

  • 46. SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction.
    Zhao Q; Xie D; Liu H; Wang F; Yan GY; Chen X
    Oncotarget; 2018 Jan; 9(2):1826-1842. PubMed ID: 29416734
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Prediction of Small Molecule-MicroRNA Associations by Sparse Learning and Heterogeneous Graph Inference.
    Yin J; Chen X; Wang CC; Zhao Y; Sun YZ
    Mol Pharm; 2019 Jul; 16(7):3157-3166. PubMed ID: 31136190
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Predicting microRNA-disease associations using bipartite local models and hubness-aware regression.
    Chen X; Cheng JY; Yin J
    RNA Biol; 2018; 15(9):1192-1205. PubMed ID: 30196756
    [TBL] [Abstract][Full Text] [Related]  

  • 49. 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]  

  • 50. 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]  

  • 51. MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA-Disease Association Prediction.
    Ni J; Li L; Wang Y; Ji C; Zheng C
    Genes (Basel); 2022 Jun; 13(6):. PubMed ID: 35741782
    [TBL] [Abstract][Full Text] [Related]  

  • 52. 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]  

  • 53. 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]  

  • 54. Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction.
    Qu J; Song Z; Cheng X; Jiang Z; Zhou J
    PeerJ; 2023; 11():e15889. PubMed ID: 37641598
    [TBL] [Abstract][Full Text] [Related]  

  • 55. SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
    Li L; Gao Z; Wang YT; Zhang MW; Ni JC; Zheng CH; Su Y
    PLoS Comput Biol; 2021 Jul; 17(7):e1009165. PubMed ID: 34252084
    [TBL] [Abstract][Full Text] [Related]  

  • 56. LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.
    Wang L; You ZH; Chen X; Li YM; Dong YN; Li LP; Zheng K
    PLoS Comput Biol; 2019 Mar; 15(3):e1006865. PubMed ID: 30917115
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model.
    Ji BY; You ZH; Cheng L; Zhou JR; Alghazzawi D; Li LP
    Sci Rep; 2020 Apr; 10(1):6658. PubMed ID: 32313121
    [TBL] [Abstract][Full Text] [Related]  

  • 58. A vector projection similarity-based method for miRNA-disease association prediction.
    Xie G; Xie W; Gu G; Lin Z; Chen R; Liu S; Yu J
    Anal Biochem; 2024 Apr; 687():115431. PubMed ID: 38123111
    [TBL] [Abstract][Full Text] [Related]  

  • 59. BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction.
    Chen X; Xie D; Wang L; Zhao Q; You ZH; Liu H
    Bioinformatics; 2018 Sep; 34(18):3178-3186. PubMed ID: 29701758
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Uncover miRNA-Disease Association by Exploiting Global Network Similarity.
    Chen M; Lu X; Liao B; Li Z; Cai L; Gu C
    PLoS One; 2016; 11(12):e0166509. PubMed ID: 27907011
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 23.