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For: Chierici M, Francescatto M, Bussola N, Jurman G, Furlanello C. Predictability of drug-induced liver injury by machine learning. Biol Direct 2020;15:3. [PMID: 32054490 DOI: 10.1186/s13062-020-0259-4] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
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3 Di Nardo G, Di Venere A, Zhang C, Nicolai E, Castrignanò S, Di Paola L, Gilardi G, Mei G. Polymorphism on human aromatase affects protein dynamics and substrate binding: spectroscopic evidence. Biol Direct 2021;16:8. [PMID: 33902660 DOI: 10.1186/s13062-021-00292-9] [Reference Citation Analysis]
4 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]
5 Butera A, Melino G, Amelio I. Epigenetic "Drivers" of Cancer. J Mol Biol 2021;433:167094. [PMID: 34119490 DOI: 10.1016/j.jmb.2021.167094] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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7 Gao B, Wang L, Zhang N, Han M, Zhang Y, Liu H, Sun D, Liu Y. Screening Novel Drug Candidates for Kidney Renal Clear Cell Carcinoma Treatment: A Study on Differentially Expressed Genes through the Connectivity Map Database. Kidney Blood Press Res 2021;46:702-13. [PMID: 34818247 DOI: 10.1159/000518437] [Reference Citation Analysis]
8 Rugolo F, Bazan NG, Calandria J, Jun B, Raschellà G, Melino G, Agostini M. The expression of ELOVL4, repressed by MYCN, defines neuroblastoma patients with good outcome. Oncogene 2021. [PMID: 34333551 DOI: 10.1038/s41388-021-01959-3] [Reference Citation Analysis]
9 Lesiński W, Mnich K, Rudnicki WR. Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports. Front Genet 2021;12:661075. [PMID: 34276771 DOI: 10.3389/fgene.2021.661075] [Reference Citation Analysis]
10 Jaganathan K, Tayara H, Chong KT. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. Int J Mol Sci 2021;22:8073. [PMID: 34360838 DOI: 10.3390/ijms22158073] [Reference Citation Analysis]
11 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]
12 Mammarella E, Zampieri C, Panatta E, Melino G, Amelio I. NUAK2 and RCan2 participate in the p53 mutant pro-tumorigenic network. Biol Direct 2021;16:11. [PMID: 34348766 DOI: 10.1186/s13062-021-00296-5] [Reference Citation Analysis]
13 Yu L, Jing R, Liu F, Luo J, Li Y. DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm. Mol Ther Nucleic Acids 2020;22:862-70. [PMID: 33230481 DOI: 10.1016/j.omtn.2020.10.005] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
14 Marchetti P, Antonov A, Anemona L, Vangapandou C, Montanaro M, Botticelli A, Mauriello A, Melino G, Catani MV. New immunological potential markers for triple negative breast cancer: IL18R1, CD53, TRIM, Jaw1, LTB, PTPRCAP. Discov Onc 2021;12. [DOI: 10.1007/s12672-021-00401-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Ganini C, Amelio I, Bertolo R, Bove P, Buonomo OC, Candi E, Cipriani C, Di Daniele N, Juhl H, Mauriello A, Marani C, Marshall J, Melino S, Marchetti P, Montanaro M, Natale ME, Novelli F, Palmieri G, Piacentini M, Rendina EA, Roselli M, Sica G, Tesauro M, Rovella V, Tisone G, Shi Y, Wang Y, Melino G. Global mapping of cancers: The Cancer Genome Atlas and beyond. Mol Oncol 2021. [PMID: 34245122 DOI: 10.1002/1878-0261.13056] [Reference Citation Analysis]
16 Mora JR, Marrero-Ponce Y, García-Jacas CR, Suarez Causado A. Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches. Chem Res Toxicol 2020;33:1855-73. [PMID: 32406679 DOI: 10.1021/acs.chemrestox.0c00030] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
17 Della-Morte D, Pacifici F, Ricordi C, Massoud R, Rovella V, Proietti S, Iozzo M, Lauro D, Bernardini S, Bonassi S, Di Daniele N. Low level of plasminogen increases risk for mortality in COVID-19 patients. Cell Death Dis 2021;12:773. [PMID: 34354045 DOI: 10.1038/s41419-021-04070-3] [Reference Citation Analysis]
18 Zhong T, Zhuang Z, Dong X, Wong KH, Wong WT, Wang J, He D, Liu S. Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study. JMIR Med Inform 2021;9:e29226. [PMID: 34283036 DOI: 10.2196/29226] [Reference Citation Analysis]