BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

327 related articles for article (PubMed ID: 27392365)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 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. 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]  

  • 6. A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder.
    Ji C; Wang Y; Gao Z; Li L; Ni J; Zheng C
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(4):2049-2059. PubMed ID: 33735084
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information.
    Ji BY; You ZH; Chen ZH; Wong L; Yi HC
    BMC Bioinformatics; 2020 Sep; 21(1):401. PubMed ID: 32912137
    [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. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations.
    Zhang W; Li Z; Guo W; Yang W; Huang F
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(2):405-415. PubMed ID: 31369383
    [TBL] [Abstract][Full Text] [Related]  

  • 15. SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.
    Qu Y; Zhang H; Liang C; Ding P; Luo J
    J Cell Mol Med; 2018 Oct; 22(10):5109-5120. PubMed ID: 30030889
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improved low-rank matrix recovery method for predicting miRNA-disease association.
    Peng L; Peng M; Liao B; Huang G; Liang W; Li K
    Sci Rep; 2017 Jul; 7(1):6007. PubMed ID: 28729528
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy.
    Wang CC; Chen X; Yin J; Qu J
    RNA Biol; 2019 Mar; 16(3):257-269. PubMed ID: 30646823
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Identifying human microRNA-disease associations by a new diffusion-based method.
    Liao B; Ding S; Chen H; Li Z; Cai L
    J Bioinform Comput Biol; 2015 Aug; 13(4):1550014. PubMed ID: 26004789
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 17.