170 related articles for article (PubMed ID: 37446675)
1. Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.
Hu X; Yin Z; Zeng Z; Peng Y
Molecules; 2023 Jun; 28(13):. PubMed ID: 37446675
[TBL] [Abstract][Full Text] [Related]
2. Prediction of potential miRNA-disease associations based on stacked autoencoder.
Wang CC; Li TH; Huang L; Chen X
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35176761
[TBL] [Abstract][Full Text] [Related]
3. SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder.
Wang S; Lin B; Zhang Y; Qiao S; Wang F; Wu W; Ren C
Cells; 2022 Dec; 11(24):. PubMed ID: 36552748
[TBL] [Abstract][Full Text] [Related]
4. Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder.
Liu W; Lin H; Huang L; Peng L; Tang T; Zhao Q; Yang L
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35325038
[TBL] [Abstract][Full Text] [Related]
5. SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.
Liu D; Huang Y; Nie W; Zhang J; Deng L
BMC Bioinformatics; 2021 Apr; 22(1):219. PubMed ID: 33910505
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Predicting miRNA-Disease Associations Through Deep Autoencoder With Multiple Kernel Learning.
Zhou F; Yin MM; Jiao CN; Zhao JX; Zheng CH; Liu JX
IEEE Trans Neural Netw Learn Syst; 2023 Sep; 34(9):5570-5579. PubMed ID: 34860656
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs.
Xiao Q; Zhang N; Luo J; Dai J; Tang X
Brief Bioinform; 2021 Mar; 22(2):2043-2057. PubMed ID: 32186712
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. 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]
17. DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder.
Peng L; Tu Y; Huang L; Li Y; Fu X; Chen X
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36377749
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. NMCMDA: neural multicategory MiRNA-disease association prediction.
Wang J; Li J; Yue K; Wang L; Ma Y; Li Q
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33778850
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
