BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

246 related articles for article (PubMed ID: 34837953)

  • 1. Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations.
    Zhou F; Yin MM; Jiao CN; Cui Z; Zhao JX; Liu JX
    BMC Bioinformatics; 2021 Nov; 22(1):573. PubMed ID: 34837953
    [TBL] [Abstract][Full Text] [Related]  

  • 2. NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations.
    Gao YL; Cui Z; Liu JX; Wang J; Zheng CH
    BMC Bioinformatics; 2019 Jun; 20(1):353. PubMed ID: 31234797
    [TBL] [Abstract][Full Text] [Related]  

  • 3. RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations.
    Cui Z; Liu JX; Gao YL; Zheng CH; Wang J
    BMC Bioinformatics; 2019 Dec; 20(Suppl 25):686. PubMed ID: 31874608
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations.
    Zhou F; Yin MM; Zhao JX; Shang J; Liu JX
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):52-60. PubMed ID: 34882558
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. MicroRNA-disease association prediction by matrix tri-factorization.
    Li H; Guo Y; Cai M; Li L
    BMC Genomics; 2020 Nov; 21(Suppl 10):617. PubMed ID: 33208088
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations.
    Wu TR; Yin MM; Jiao CN; Gao YL; Kong XZ; Liu JX
    BMC Bioinformatics; 2020 Oct; 21(1):454. PubMed ID: 33054708
    [TBL] [Abstract][Full Text] [Related]  

  • 10. IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.
    Ha J; Park C; Park C; Park S
    J Biomed Inform; 2020 Feb; 102():103358. PubMed ID: 31857202
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Dual-network sparse graph regularized matrix factorization for predicting miRNA-disease associations.
    Gao MM; Cui Z; Gao YL; Liu JX; Zheng CH
    Mol Omics; 2019 Apr; 15(2):130-137. PubMed ID: 30723850
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.
    Han GS; Gao Q; Peng LZ; Tang J
    Interdiscip Sci; 2024 Mar; 16(1):176-191. PubMed ID: 38099958
    [TBL] [Abstract][Full Text] [Related]  

  • 14. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.
    Chu Y; Wang X; Dai Q; Wang Y; Wang Q; Peng S; Wei X; Qiu J; Salahub DR; Xiong Y; Wei DQ
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34009265
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.
    Zhong J; Zhou W; Kang J; Fang Z; Xie M; Xiao Q; Peng W
    Interdiscip Sci; 2022 Jun; 14(2):607-622. PubMed ID: 35428965
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of miRNA-disease associations by neural network-based deep matrix factorization.
    Qu Q; Chen X; Ning B; Zhang X; Nie H; Zeng L; Chen H; Fu X
    Methods; 2023 Apr; 212():1-9. PubMed ID: 36813017
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.
    Wang L; Zhong C
    Biomed Res Int; 2021; 2021():6652948. PubMed ID: 33681362
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.
    Nguyen VT; Le TTK; Nguyen TQV; Tran DH
    BMC Med Genomics; 2021 Nov; 14(Suppl 3):225. PubMed ID: 34789252
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.