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For: Gong Y, Niu Y, Zhang W, Li X. A network embedding-based multiple information integration method for the MiRNA-disease association prediction. BMC Bioinformatics 2019;20:468. [PMID: 31510919 DOI: 10.1186/s12859-019-3063-3] [Cited by in Crossref: 30] [Cited by in F6Publishing: 22] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Hou R, Wang L, Wu YJ. Predicting ATP-Binding Cassette Transporters Using the Random Forest Method. Front Genet 2020;11:156. [PMID: 32269586 DOI: 10.3389/fgene.2020.00156] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Zhou YK, Shen ZA, Yu H, Luo T, Gao Y, Du PF. Predicting lncRNA-Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model. Front Genet 2019;10:1341. [PMID: 32038709 DOI: 10.3389/fgene.2019.01341] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
3 Zhao C, Qiu Y, Zhou S, Liu S, Zhang W, Niu Y. Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction. BMC Genomics 2020;21:867. [PMID: 33334307 DOI: 10.1186/s12864-020-07238-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
4 Xiang J, Zhang NR, Zhang JS, Lv XY, Li M. PrGeFNE: Predicting disease-related genes by fast network embedding. Methods 2021;192:3-12. [PMID: 32610158 DOI: 10.1016/j.ymeth.2020.06.015] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
5 Dai Q, Chu Y, Li Z, Zhao Y, Mao X, Wang Y, Xiong Y, Wei DQ. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Comput Biol Med 2021;136:104706. [PMID: 34371319 DOI: 10.1016/j.compbiomed.2021.104706] [Reference Citation Analysis]
6 Huang Q, Zhou W, Guo F, Xu L, Zhang L. 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning. PeerJ 2021;9:e10813. [PMID: 33604189 DOI: 10.7717/peerj.10813] [Reference Citation Analysis]
7 Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021:bbab165. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Reference Citation Analysis]
8 Li HY, You ZH, Wang L, Yan X, Li ZW. DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association. Mol Ther 2021;29:1501-11. [PMID: 33429082 DOI: 10.1016/j.ymthe.2021.01.003] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Liu F, Peng L, Tian G, Yang J, Chen H, Hu Q, Liu X, Zhou L. Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk. Front Bioeng Biotechnol 2020;8:131. [PMID: 32258003 DOI: 10.3389/fbioe.2020.00131] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Li Q, Zhou W, Wang D, Wang S, Li Q. Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model. Front Bioeng Biotechnol 2020;8:892. [PMID: 32903381 DOI: 10.3389/fbioe.2020.00892] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
11 Zeng R, Liao M. Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications. Front Bioeng Biotechnol 2020;8:274. [PMID: 32373597 DOI: 10.3389/fbioe.2020.00274] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
12 Wang L, Zhong C. Prediction of miRNA-Disease Association Using Deep Collaborative Filtering. Biomed Res Int 2021;2021:6652948. [PMID: 33681362 DOI: 10.1155/2021/6652948] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Qian Y, Jiang L, Ding Y, Tang J, Guo F. A sequence-based multiple kernel model for identifying DNA-binding proteins. BMC Bioinformatics 2021;22:291. [PMID: 34058979 DOI: 10.1186/s12859-020-03875-x] [Reference Citation Analysis]
14 Huang F, Qiu Y, Li Q, Liu S, Ni F. Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization. Front Bioeng Biotechnol 2020;8:218. [PMID: 32373595 DOI: 10.3389/fbioe.2020.00218] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
15 Liu B, Zhu X, Zhang L, Liang Z, Li Z. Combined embedding model for MiRNA-disease association prediction. BMC Bioinformatics 2021;22:161. [PMID: 33765909 DOI: 10.1186/s12859-021-04092-w] [Reference Citation Analysis]
16 Tan H, Sun Q, Li G, Xiao Q, Ding P, Luo J, Liang C. Multiview Consensus Graph Learning for lncRNA-Disease Association Prediction. Front Genet 2020;11:89. [PMID: 32153646 DOI: 10.3389/fgene.2020.00089] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Wang W, Lv H, Zhao Y, Liu D, Wang Y, Zhang Y. DLS: A Link Prediction Method Based on Network Local Structure for Predicting Drug-Protein Interactions. Front Bioeng Biotechnol 2020;8:330. [PMID: 32391341 DOI: 10.3389/fbioe.2020.00330] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
18 Zhang L, Liu B, Li Z, Zhu X, Liang Z, An J. Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model. BMC Bioinformatics 2020;21:470. [PMID: 33087064 DOI: 10.1186/s12859-020-03765-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
19 Liu D, Huang Y, Nie W, Zhang J, Deng L. SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost. BMC Bioinformatics 2021;22:219. [PMID: 33910505 DOI: 10.1186/s12859-021-04135-2] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
20 Wang J, Li J, Yue K, Wang L, Ma Y, Li Q. NMCMDA: neural multicategory MiRNA-disease association prediction. Brief Bioinform 2021:bbab074. [PMID: 33778850 DOI: 10.1093/bib/bbab074] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 Cai J, Wang D, Chen R, Niu Y, Ye X, Su R, Xiao G, Wei L. A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol. Front Bioeng Biotechnol 2020;8:502. [PMID: 32582654 DOI: 10.3389/fbioe.2020.00502] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
22 Chen Q, Meng Z, Su R. WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy. Front Bioeng Biotechnol 2020;8:496. [PMID: 32548100 DOI: 10.3389/fbioe.2020.00496] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
23 Ai C, Yang H, Ding Y, Tang J, Guo F. A multi-layer multi-kernel neural network for determining associations between non-coding RNAs and diseases. Neurocomputing 2022;493:91-105. [DOI: 10.1016/j.neucom.2022.04.068] [Reference Citation Analysis]
24 Liang G, Wu J, Xu L. A prognosis-related based method for miRNA selection on liver hepatocellular carcinoma prediction. Comput Biol Chem 2021;91:107433. [PMID: 33540232 DOI: 10.1016/j.compbiolchem.2020.107433] [Reference Citation Analysis]