BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

179 related articles for article (PubMed ID: 38553698)

  • 21. Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks.
    Zhao H; Li Z; You ZH; Nie R; Zhong T
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1298-1307. PubMed ID: 36067101
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.
    Jiao CN; Zhou F; Liu BM; Zheng CH; Liu JX; Gao YL
    IEEE J Biomed Health Inform; 2024 Feb; 28(2):1110-1121. PubMed ID: 38055359
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network.
    Liu M; Yang J; Wang J; Deng L
    BMC Med Genomics; 2020 Oct; 13(Suppl 10):153. PubMed ID: 33087118
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network.
    Lu P; Jiang J
    Comput Biol Chem; 2024 Jun; 110():108085. PubMed ID: 38754260
    [TBL] [Abstract][Full Text] [Related]  

  • 26. 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]  

  • 27. 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]  

  • 28. 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]  

  • 29. Adaptive deep propagation graph neural network for predicting miRNA-disease associations.
    Hu H; Zhao H; Zhong T; Dong X; Wang L; Han P; Li Z
    Brief Funct Genomics; 2023 Nov; 22(5):453-462. PubMed ID: 37078739
    [TBL] [Abstract][Full Text] [Related]  

  • 30. SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction.
    Ma M; Na S; Zhang X; Chen C; Xu J
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36037084
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information.
    Han G; Kuang Z; Deng L
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(5):2926-2937. PubMed ID: 34410928
    [TBL] [Abstract][Full Text] [Related]  

  • 33. FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.
    Li J; Li Z; Nie R; You Z; Bao W
    Mol Genet Genomics; 2020 Sep; 295(5):1197-1209. PubMed ID: 32500265
    [TBL] [Abstract][Full Text] [Related]  

  • 34. GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.
    Li L; Wang YT; Ji CM; Zheng CH; Ni JC; Su YS
    PLoS Comput Biol; 2021 Dec; 17(12):e1009655. PubMed ID: 34890410
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression.
    Zhou S; Wang S; Wu Q; Azim R; Li W
    Comput Biol Chem; 2020 Apr; 85():107200. PubMed ID: 32058946
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction.
    Xuan P; Wang D; Cui H; Zhang T; Nakaguchi T
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34634106
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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]  

  • 38. Predicting miRNA-disease associations based on graph attention network with multi-source information.
    Li G; Fang T; Zhang Y; Liang C; Xiao Q; Luo J
    BMC Bioinformatics; 2022 Jun; 23(1):244. PubMed ID: 35729531
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Predicting miRNA-disease associations based on PPMI and attention network.
    Xie X; Wang Y; He K; Sheng N
    BMC Bioinformatics; 2023 Mar; 24(1):113. PubMed ID: 36959547
    [TBL] [Abstract][Full Text] [Related]  

  • 40. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.
    Chen X; Yin J; Qu J; Huang L
    PLoS Comput Biol; 2018 Aug; 14(8):e1006418. PubMed ID: 30142158
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.