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.
213 related articles for article (PubMed ID: 32726399)
1. AEMDA: inferring miRNA-disease associations based on deep autoencoder. Ji C; Gao Z; Ma X; Wu Q; Ni J; Zheng C Bioinformatics; 2021 Apr; 37(1):66-72. PubMed ID: 32726399 [TBL] [Abstract][Full Text] [Related]
2. 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]
3. Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases. Sheng N; Wang Y; Huang L; Gao L; Cao Y; Xie X; Fu Y Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37529914 [TBL] [Abstract][Full Text] [Related]
4. 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]
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. A learning-based framework for miRNA-disease association identification using neural networks. Peng J; Hui W; Li Q; Chen B; Hao J; Jiang Q; Shang X; Wei Z Bioinformatics; 2019 Nov; 35(21):4364-4371. PubMed ID: 30977780 [TBL] [Abstract][Full Text] [Related]
7. Predicting miRNA-disease association based on inductive matrix completion. Chen X; Wang L; Qu J; Guan NN; Li JQ Bioinformatics; 2018 Dec; 34(24):4256-4265. PubMed ID: 29939227 [TBL] [Abstract][Full Text] [Related]
8. Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information. Lou Z; Cheng Z; Li H; Teng Z; Liu Y; Tian Z Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35524503 [TBL] [Abstract][Full Text] [Related]
9. A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Xiao Q; Luo J; Liang C; Cai J; Ding P Bioinformatics; 2018 Jan; 34(2):239-248. PubMed ID: 28968779 [TBL] [Abstract][Full Text] [Related]
10. Prediction of potential disease-associated microRNAs using structural perturbation method. Zeng X; Liu L; Lü L; Zou Q Bioinformatics; 2018 Jul; 34(14):2425-2432. PubMed ID: 29490018 [TBL] [Abstract][Full Text] [Related]
11. 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]
12. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Dai Q; Chu Y; Li Z; Zhao Y; Mao X; Wang Y; Xiong Y; Wei DQ Comput Biol Med; 2021 Sep; 136():104706. PubMed ID: 34371319 [TBL] [Abstract][Full Text] [Related]
13. DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation. Liu Y; Zhang R; Dong X; Yang H; Li J; Cao H; Tian J; Zhang Y BMC Bioinformatics; 2024 Mar; 25(1):139. PubMed ID: 38553698 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features. Guo Y; Zhou D; Ruan X; Cao J Neural Netw; 2023 Aug; 165():491-505. PubMed ID: 37336034 [TBL] [Abstract][Full Text] [Related]
18. Variational graph auto-encoders for miRNA-disease association prediction. Ding Y; Tian LP; Lei X; Liao B; Wu FX Methods; 2021 Aug; 192():25-34. PubMed ID: 32798654 [TBL] [Abstract][Full Text] [Related]
19. 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]
20. miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences. Zhang T; Ju L; Zhai J; Song Y; Song J; Ma C Methods Mol Biol; 2019; 1932():89-97. PubMed ID: 30701493 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]