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Cited by in F6Publishing
For: Zhang X, Yin J, Zhang X. A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network. Genes (Basel) 2018;9:E139. [PMID: 29498680 DOI: 10.3390/genes9030139] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 2.3] [Reference Citation Analysis]
Number Citing Articles
1 Ouyang D, Liang Y, Wang J, Liu X, Xie S, Miao R, Ai N, Li L, Dang Q. Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization. Brief Bioinform 2022:bbac390. [PMID: 36168938 DOI: 10.1093/bib/bbac390] [Reference Citation Analysis]
2 Ouyang D, Miao R, Wang J, Liu X, Xie S, Ai N, Dang Q, Liang Y. Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition. Front Bioeng Biotechnol 2022;10:911769. [DOI: 10.3389/fbioe.2022.911769] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Yu N, Liu ZP, Gao R. Predicting multiple types of MicroRNA-disease associations based on tensor factorization and label propagation. Comput Biol Med 2022;146:105558. [PMID: 35525071 DOI: 10.1016/j.compbiomed.2022.105558] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
4 Tian Q, Zhou S, Wu Q. A miRNA-Disease Association Identification Method Based on Reliable Negative Sample Selection and Improved Single-Hidden Layer Feedforward Neural Network. Information 2022;13:108. [DOI: 10.3390/info13030108] [Reference Citation Analysis]
5 Zheng X, Zhang C, Wan C. MiRNA-Disease association prediction via non-negative matrix factorization based matrix completion. Signal Processing 2022;190:108312. [DOI: 10.1016/j.sigpro.2021.108312] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Yu DL, Yu ZG, Han GS, Li J, Anh V. Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction. Biomedicines 2021;9:1152. [PMID: 34572337 DOI: 10.3390/biomedicines9091152] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Li A, Deng Y, Tan Y, Chen M. A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method. PLoS One 2021;16:e0252971. [PMID: 34138933 DOI: 10.1371/journal.pone.0252971] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 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: 4] [Reference Citation Analysis]
9 Huang F, Yue X, Xiong Z, Yu Z, Liu S, Zhang W. Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations. Brief Bioinform 2021;22:bbaa140. [PMID: 32725161 DOI: 10.1093/bib/bbaa140] [Cited by in Crossref: 12] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]
10 Kim SW, Lee YG, Tama BA, Lee S. Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method. Applied Sciences 2020;10:3832. [DOI: 10.3390/app10113832] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 Liu Y, Li X, Feng X, Wang L. A Novel Neighborhood-Based Computational Model for Potential MiRNA-Disease Association Prediction. Comput Math Methods Med 2019;2019:5145646. [PMID: 30800172 DOI: 10.1155/2019/5145646] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]