153 related articles for article (PubMed ID: 36176298)
21. RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion.
Wang SH; Zhao Y; Wang CC; Chu F; Miao LY; Zhang L; Zhuo L; Chen X
Comput Biol Med; 2024 Mar; 171():108177. PubMed ID: 38422957
[TBL] [Abstract][Full Text] [Related]
22. MvKFN-MDA: Multi-view Kernel Fusion Network for miRNA-disease association prediction.
Li J; Liu T; Wang J; Li Q; Ning C; Yang Y
Artif Intell Med; 2021 Aug; 118():102115. PubMed ID: 34412838
[TBL] [Abstract][Full Text] [Related]
23. A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square.
Zhao Y; Chen X; Yin J
Front Genet; 2018; 9():324. PubMed ID: 30186308
[TBL] [Abstract][Full Text] [Related]
24. MicroRNAs and complex diseases: from experimental results to computational models.
Chen X; Xie D; Zhao Q; You ZH
Brief Bioinform; 2019 Mar; 20(2):515-539. PubMed ID: 29045685
[TBL] [Abstract][Full Text] [Related]
25. GRMDA: Graph Regression for MiRNA-Disease Association Prediction.
Chen X; Yang JR; Guan NN; Li JQ
Front Physiol; 2018; 9():92. PubMed ID: 29515453
[TBL] [Abstract][Full Text] [Related]
26. A random forest based computational model for predicting novel lncRNA-disease associations.
Yao D; Zhan X; Zhan X; Kwoh CK; Li P; Wang J
BMC Bioinformatics; 2020 Mar; 21(1):126. PubMed ID: 32216744
[TBL] [Abstract][Full Text] [Related]
27. MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.
Zhang Y; Chen M; Cheng X; Wei H
Front Genet; 2020; 11():389. PubMed ID: 32425980
[TBL] [Abstract][Full Text] [Related]
28. MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.
Wang YT; Wu QW; Gao Z; Ni JC; Zheng CH
BMC Med Inform Decis Mak; 2021 Apr; 21(Suppl 1):133. PubMed ID: 33882934
[TBL] [Abstract][Full Text] [Related]
29. Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.
Nie R; Li Z; You ZH; Bao W; Li J
BMC Med Inform Decis Mak; 2021 Aug; 21(Suppl 1):254. PubMed ID: 34461870
[TBL] [Abstract][Full Text] [Related]
30. DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations.
Zheng K; You ZH; Wang L; Zhou Y; Li LP; Li ZW
Mol Ther Nucleic Acids; 2020 Mar; 19():602-611. PubMed ID: 31931344
[TBL] [Abstract][Full Text] [Related]
31. GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction.
Li Z; Jiang K; Qin S; Zhong Y; Elofsson A
PLoS Comput Biol; 2021 Jun; 17(6):e1009048. PubMed ID: 34081706
[TBL] [Abstract][Full Text] [Related]
32. BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction.
Zhu X; Wang X; Zhao H; Pei T; Kuang L; Wang L
Front Genet; 2020; 11():384. PubMed ID: 32425979
[TBL] [Abstract][Full Text] [Related]
33. Ensemble of decision tree reveals potential miRNA-disease associations.
Chen X; Zhu CC; Yin J
PLoS Comput Biol; 2019 Jul; 15(7):e1007209. PubMed ID: 31329575
[TBL] [Abstract][Full Text] [Related]
34. Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.
Yao D; Zhang T; Zhan X; Zhang S; Zhan X; Zhang C
Front Genet; 2022; 13():995532. PubMed ID: 36092871
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.
Huang D; An J; Zhang L; Liu B
BMC Bioinformatics; 2022 Jul; 23(1):299. PubMed ID: 35879658
[TBL] [Abstract][Full Text] [Related]
37. Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges.
Xuan P; Li L; Zhang T; Zhang Y; Song Y
Molecules; 2019 Aug; 24(17):. PubMed ID: 31455026
[TBL] [Abstract][Full Text] [Related]
38. Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction.
Xuan P; Wang D; Cui H; Zhang T; Nakaguchi T
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34634106
[TBL] [Abstract][Full Text] [Related]
39. FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.
Li X; Lin Y; Gu C; Yang J
BMC Syst Biol; 2019 Apr; 13(Suppl 2):26. PubMed ID: 30953512
[TBL] [Abstract][Full Text] [Related]
40. A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction.
Chen X; Jiang ZC; Xie D; Huang DS; Zhao Q; Yan GY; You ZH
Mol Biosyst; 2017 May; 13(6):1202-1212. PubMed ID: 28470244
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
