These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
185 related articles for article (PubMed ID: 39154195)
1. PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs. Chen L; Gu J; Zhou B Brief Bioinform; 2024 Jul; 25(5):. PubMed ID: 39154195 [TBL] [Abstract][Full Text] [Related]
2. PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features. Yan C; Duan G; Li N; Zhang L; Wu FX; Wang J Bioinformatics; 2022 Apr; 38(8):2226-2234. PubMed ID: 35150255 [TBL] [Abstract][Full Text] [Related]
3. MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization. Xu M; Chen Y; Xu Z; Zhang L; Jiang H; Pian C Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35183063 [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. DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks. Bai T; Yan K; Liu B Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37332057 [TBL] [Abstract][Full Text] [Related]
6. NGCICM: A Novel Deep Learning-Based Method for Predicting circRNA-miRNA Interactions. Ma Z; Kuang Z; Deng L IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(5):3080-3092. PubMed ID: 37027645 [TBL] [Abstract][Full Text] [Related]
7. miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides. Meher PK; Satpathy S; Rao AR Sci Rep; 2020 Sep; 10(1):14557. PubMed ID: 32884018 [TBL] [Abstract][Full Text] [Related]
8. 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]
9. A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations. Wen S; Liu Y; Yang G; Chen W; Wu H; Zhu X; Wang Y Sci Rep; 2024 Sep; 14(1):20490. PubMed ID: 39227405 [TBL] [Abstract][Full Text] [Related]
10. A graph auto-encoder model for miRNA-disease associations prediction. Li Z; Li J; Nie R; You ZH; Bao W Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 34293850 [TBL] [Abstract][Full Text] [Related]
11. Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion. Jin Z; Wang M; Tang C; Zheng X; Zhang W; Sha X; An S Comput Biol Med; 2024 Feb; 169():107904. PubMed ID: 38181611 [TBL] [Abstract][Full Text] [Related]
12. MGFmiRNAloc: Predicting miRNA Subcellular Localization Using Molecular Graph Feature and Convolutional Block Attention Module. Liang Y; You X; Zhang Z; Qiu S; Li S; Fu L IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(5):1348-1357. PubMed ID: 38557611 [TBL] [Abstract][Full Text] [Related]
13. MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding. Dong B; Sun W; Xu D; Wang G; Zhang T Comput Biol Med; 2023 Dec; 167():107585. PubMed ID: 37890424 [TBL] [Abstract][Full Text] [Related]
14. Identification of MiRNA-Disease Associations Based on Information of Multi-Module and Meta-Path. Li Z; Huang X; Shi Y; Zou X; Li Z; Dai Z Molecules; 2022 Jul; 27(14):. PubMed ID: 35889314 [TBL] [Abstract][Full Text] [Related]
15. MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction. Ji B; Zou H; Xu L; Xie X; Peng S Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38605642 [TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction. Li J; Zhang S; Liu T; Ning C; Zhang Z; Zhou W Bioinformatics; 2020 Apr; 36(8):2538-2546. PubMed ID: 31904845 [TBL] [Abstract][Full Text] [Related]
19. Toward drug-miRNA resistance association prediction by positional encoding graph neural network and multi-channel neural network. Zhao C; Wang H; Qi W; Liu S Methods; 2022 Nov; 207():81-89. PubMed ID: 36167292 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]