218 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]
14. Adaptive boosting-based computational model for predicting potential miRNA-disease associations.
Zhao Y; Chen X; Yin J
Bioinformatics; 2019 Nov; 35(22):4730-4738. PubMed ID: 31038664
[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]