BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

139 related articles for article (PubMed ID: 32092012)

  • 1. Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization.
    Luo J; Shen C; Lai Z; Cai J; Ding P
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2535-2545. PubMed ID: 32092012
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association.
    Zhao Y; Chen X; Yin J; Qu J
    RNA Biol; 2020 Feb; 17(2):281-291. PubMed ID: 31739716
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization.
    Chen X; Zhou C; Wang CC; Zhao Y
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34404088
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations.
    Wang SH; Wang CC; Huang L; Miao LY; Chen X
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34864865
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Heterogeneous graph inference with range constrainted L
    Wang S; Liu T; Ren C; Zhao Y; Qiao S; Zhang Y; Pang S
    Comput Biol Chem; 2024 Jun; 110():108078. PubMed ID: 38677013
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. Predicting miRNA-Disease Associations Based On Multi-View Variational Graph Auto-Encoder With Matrix Factorization.
    Ding Y; Lei X; Liao B; Wu FX
    IEEE J Biomed Health Inform; 2022 Jan; 26(1):446-457. PubMed ID: 34111017
    [TBL] [Abstract][Full Text] [Related]  

  • 11. AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion.
    Wang S; Ren C; Zhang Y; Pang S; Qiao S; Wu W; Lin B
    Cells; 2023 Apr; 12(8):. PubMed ID: 37190032
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities.
    Lan W; Wang J; Li M; Liu J; Wu FX; Pan Y
    IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(6):1774-1782. PubMed ID: 27392365
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm.
    Wang S; Liu T; Ren C; Wu W; Zhao Z; Pang S; Zhang Y
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37366591
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge.
    Shen C; Luo J; Lai Z; Ding P
    J Chem Inf Model; 2020 Aug; 60(8):4085-4097. PubMed ID: 32648750
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.
    Deepthi K; Jereesh AS
    Mol Genet Genomics; 2020 Sep; 295(5):1305-1314. PubMed ID: 32583015
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization.
    Toprak A; Eryilmaz Dogan E
    Interdiscip Sci; 2021 Dec; 13(4):595-602. PubMed ID: 34370220
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A New Method Based on Matrix Completion and Non-Negative Matrix Factorization for Predicting Disease-Associated miRNAs.
    Gao Z; Wang YT; Wu QW; Li L; Ni JC; Zheng CH
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):763-772. PubMed ID: 32991287
    [TBL] [Abstract][Full Text] [Related]  

  • 19. LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations.
    Yin MM; Cui Z; Gao MM; Liu JX; Gao YL
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(3):1122-1129. PubMed ID: 31478868
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Identification of Small Molecule-miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks.
    Shen C; Luo J; Ouyang W; Ding P; Wu H
    J Chem Inf Model; 2020 Dec; 60(12):6709-6721. PubMed ID: 33166451
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.