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For: Ou-Yang L, Lu F, Zhang ZC, Wu M. Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Brief Bioinform 2021:bbab479. [PMID: 34864871 DOI: 10.1093/bib/bbab479] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
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1 Avşar G, Pir P. A comparative performance evaluation of imputation methods in spatially resolved transcriptomics data. Mol Omics 2022. [PMID: 36562244 DOI: 10.1039/d2mo00266c] [Reference Citation Analysis]
2 Xie X, Wang Y, Sheng N, Zhang S, Cao Y, Fu Y. Predicting miRNA-disease associations based on multi-view information fusion. Front Genet 2022;13:979815. [DOI: 10.3389/fgene.2022.979815] [Reference Citation Analysis]
3 Keyvanpour MR, Haddadi F, Mehrmolaei S. DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Computational Biology and Chemistry 2022. [DOI: 10.1016/j.compbiolchem.2022.107707] [Reference Citation Analysis]
4 Vahabi N, Michailidis G. Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Front Genet 2022;13:854752. [PMID: 35391796 DOI: 10.3389/fgene.2022.854752] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
5 Mariappan R, Jayagopal A, Sien HZ, Rajan V. Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data.. [DOI: 10.1101/2022.01.20.477057] [Reference Citation Analysis]