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Cited by in F6Publishing
For: Li Z, Huang Q, Chen X, Wang Y, Li J, Xie Y, Dai Z, Zou X. Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network. Front Chem 2019;7:924. [PMID: 31998700 DOI: 10.3389/fchem.2019.00924] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Liu J, Peng D, Li J, Dai Z, Zou X, Li Z. Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network. Molecules 2022;27:4780. [PMID: 35897954 DOI: 10.3390/molecules27154780] [Reference Citation Analysis]
2 Huo M, Peng S, Li J, Zhang Y, Qiao Y, Deng M. A Discovery Strategy for Active Compounds of Chinese Medicine Based on the Prediction Model of Compound-Disease Relationship. Journal of Oncology 2022;2022:1-11. [DOI: 10.1155/2022/8704784] [Reference Citation Analysis]
3 Zhang Y, Lei X, Pan Y, Wu FX. Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks. Front Pharmacol 2022;13:872785. [PMID: 35620297 DOI: 10.3389/fphar.2022.872785] [Reference Citation Analysis]
4 Xuan P, Meng X, Gao L, Zhang T, Nakaguchi T. Heterogeneous multi-scale neighbor topologies enhanced drug-disease association prediction. Brief Bioinform 2022:bbac123. [PMID: 35393616 DOI: 10.1093/bib/bbac123] [Reference Citation Analysis]
5 Wang L, Tan Y, Yang X, Kuang L, Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Brief Bioinform 2022:bbac080. [PMID: 35325024 DOI: 10.1093/bib/bbac080] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Zhao BW, Hu L, You ZH, Wang L, Su XR. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform 2021:bbab515. [PMID: 34891172 DOI: 10.1093/bib/bbab515] [Reference Citation Analysis]
7 Wang F, Lei X, Liao B, Wu FX. Predicting drug-drug interactions by graph convolutional network with multi-kernel. Brief Bioinform 2021:bbab511. [PMID: 34864856 DOI: 10.1093/bib/bbab511] [Reference Citation Analysis]
8 Gao L, Cui H, Zhang T, Sheng N, Xuan P. Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets. Brief Bioinform 2021:bbab467. [PMID: 34850815 DOI: 10.1093/bib/bbab467] [Reference Citation Analysis]
9 Xue L, Tang XQ. A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases. Interdiscip Sci 2021. [PMID: 33905111 DOI: 10.1007/s12539-021-00432-9] [Reference Citation Analysis]
10 Kawichai T, Suratanee A, Plaimas K. Meta-Path Based Gene Ontology Profiles for Predicting Drug-Disease Associations. IEEE Access 2021;9:41809-20. [DOI: 10.1109/access.2021.3065280] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
11 Yu Z, Huang F, Zhao X, Xiao W, Zhang W. Predicting drug-disease associations through layer attention graph convolutional network. Brief Bioinform 2021;22:bbaa243. [PMID: 33078832 DOI: 10.1093/bib/bbaa243] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]