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For: Luo P, Li Y, Tian L, Wu F, Wren J. Enhancing the prediction of disease–gene associations with multimodal deep learning. Bioinformatics 2019;35:3735-42. [DOI: 10.1093/bioinformatics/btz155] [Cited by in Crossref: 21] [Cited by in F6Publishing: 17] [Article Influence: 7.0] [Reference Citation Analysis]
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18 Wang S, Li J, Wang Y. M2PP: a novel computational model for predicting drug-targeted pathogenic proteins. BMC Bioinformatics 2022;23:7. [PMID: 34983358 DOI: 10.1186/s12859-021-04522-9] [Reference Citation Analysis]
19 Zhang Y, Xiang J, Tang L, Li J, Lu Q, Tian G, He BS, Yang J. Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity. Front Genet 2021;12:596794. [PMID: 34484285 DOI: 10.3389/fgene.2021.596794] [Reference Citation Analysis]
20 Vasighizaker A, Sharma A, Dehzangi A. A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer. PLoS One 2019;14:e0226115. [PMID: 31825992 DOI: 10.1371/journal.pone.0226115] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
21 Ding Y, Lei X, Liao B, Wu FX. Machine learning approaches for predicting biomolecule-disease associations. Brief Funct Genomics 2021;20:273-87. [PMID: 33554238 DOI: 10.1093/bfgp/elab002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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