Multiscale fusion network drives the repurposing of anticancer drugs

References

[1] 1. Kumar A, Zhang KYJ. Advances in the development of shape similarity methods and their application in drug discovery. Front Chem. 2018;6:315.

[2] Gandini E, Marcou G, Bonachera F, Varnek A, Pieraccini S, Sironi M. Molecular similarity perception based on machine-learning models. Int J Mol Sci. 2022;23(11):6114.

[3] Kang H, Hou L, Gu Y, Lu X, Li J, Li Q. Drug-disease association prediction with literature based multi-feature fusion. Front Pharmacol. 2023;14:1205144.

[4] He S, Wen Y, Yang X, et al. PIMD: an integrative approach for drug repositioning using multiple characterization fusion. Genomics Proteom Bioinform. 2020;18(5):565-581.

[5] Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42(Database issue):D1091-D1097.

[6] Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333-337.

[7] Öztürk H, Ozkirimli E, Özgür A. A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction. BMC Bioinform. 2016;17:128.

[8] Lee I, Keum J, Nam H. DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol. 2019;15(6):e1007129.

[9] Wang S, Li J, Wang D, Xu D, Jin J, Wang Y. Predicting drug-disease associations through similarity network fusion and multi-view feature projection representation. IEEE J Biomed Health Inform. 2023;27(10):5165-5176.

[10] Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27-30.

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