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Cited by in F6Publishing
For: Aguirre-Plans J, Piñero J, Souza T, Callegaro G, Kunnen SJ, Sanz F, Fernandez-Fuentes N, Furlong LI, Guney E, Oliva B. An ensemble learning approach for modeling the systems biology of drug-induced injury. Biol Direct 2021;16:5. [PMID: 33435983 DOI: 10.1186/s13062-020-00288-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Pognan F, Steger-Hartmann T, Díaz C, Blomberg N, Bringezu F, Briggs K, Callegaro G, Capella-Gutierrez S, Centeno E, Corvi J, Drew P, Drewe WC, Fernández JM, Furlong LI, Guney E, Kors JA, Mayer MA, Pastor M, Piñero J, Ramírez-Anguita JM, Ronzano F, Rowell P, Saüch-Pitarch J, Valencia A, van de Water B, van der Lei J, van Mulligen E, Sanz F. The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives. Pharmaceuticals (Basel) 2021;14:237. [PMID: 33800393 DOI: 10.3390/ph14030237] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 Adeluwa T, McGregor BA, Guo K, Hur J. Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors. Front Pharmacol 2021;12:648805. [PMID: 34483896 DOI: 10.3389/fphar.2021.648805] [Reference Citation Analysis]
3 Kurosaki K, Uesawa Y. Development of in silico prediction models for drug-induced liver malignant tumors based on the activity of molecular initiating events: Biologically interpretable features. J Toxicol Sci 2022;47:89-98. [PMID: 35236804 DOI: 10.2131/jts.47.89] [Reference Citation Analysis]