153 related articles for article (PubMed ID: 36176298)
1. A clustering-based sampling method for miRNA-disease association prediction.
Wei Z; Yao D; Zhan X; Zhang S
Front Genet; 2022; 13():995535. PubMed ID: 36176298
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Novel Human miRNA-Disease Association Inference Based on Random Forest.
Chen X; Wang CC; Yin J; You ZH
Mol Ther Nucleic Acids; 2018 Dec; 13():568-579. PubMed ID: 30439645
[TBL] [Abstract][Full Text] [Related]
4. Predicting miRNA-disease associations using an ensemble learning framework with resampling method.
Dai Q; Wang Z; Liu Z; Duan X; Song J; Guo M
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34929742
[TBL] [Abstract][Full Text] [Related]
5. Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion.
Fan C; Ding M
Sci Rep; 2023 May; 13(1):8833. PubMed ID: 37258695
[TBL] [Abstract][Full Text] [Related]
6. A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.
Peng LH; Zhou LQ; Chen X; Piao X
Front Bioeng Biotechnol; 2020; 8():40. PubMed ID: 32117922
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations.
Wang CC; Chen X; Qu J; Sun YZ; Li JQ
J Chem Inf Model; 2019 Apr; 59(4):1668-1679. PubMed ID: 30840454
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease.
Wang CC; Zhu CC; Chen X
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34676393
[TBL] [Abstract][Full Text] [Related]
12. ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations.
Chen XJ; Hua XY; Jiang ZR
BMC Bioinformatics; 2021 Jul; 22(1):358. PubMed ID: 34215183
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Predicting miRNA-disease associations based on graph random propagation network and attention network.
Zhong T; Li Z; You ZH; Nie R; Zhao H
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35079767
[TBL] [Abstract][Full Text] [Related]
15. GIMDA: Graphlet interaction-based MiRNA-disease association prediction.
Chen X; Guan NN; Li JQ; Yan GY
J Cell Mol Med; 2018 Mar; 22(3):1548-1561. PubMed ID: 29272076
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.
Zhang Y; Chen M; Cheng X; Chen Z
RSC Adv; 2019 Sep; 9(51):29747-29759. PubMed ID: 35531537
[TBL] [Abstract][Full Text] [Related]
18. Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.
Xu L; Li X; Yang Q; Tan L; Liu Q; Liu Y
Front Genet; 2022; 13():936823. PubMed ID: 35903359
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction.
Chen X; Zhou Z; Zhao Y
RNA Biol; 2018; 15(6):807-818. PubMed ID: 29619882
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]