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For: Ai H, Chen W, Zhang L, Huang L, Yin Z, Hu H, Zhao Q, Zhao J, Liu H. Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints. Toxicol Sci 2018;165:100-7. [PMID: 29788510 DOI: 10.1093/toxsci/kfy121] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 8.7] [Reference Citation Analysis]
Number Citing Articles
1 Liu L, Fu L, Zhang JW, Wei H, Ye WL, Deng ZK, Zhang L, Cheng Y, Ouyang D, Cao Q, Cao DS. Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects. Mol Pharm 2019;16:393-408. [PMID: 30475633 DOI: 10.1021/acs.molpharmaceut.8b01048] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
2 Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021;73:2546-63. [PMID: 33098140 DOI: 10.1002/hep.31603] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
3 Feng H, Zhang L, Li S, Liu L, Yang T, Yang P, Zhao J, Arkin IT, Liu H. Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints. Toxicol Lett 2021;340:4-14. [PMID: 33421549 DOI: 10.1016/j.toxlet.2021.01.002] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
4 He S, Ye T, Wang R, Zhang C, Zhang X, Sun G, Sun X. An In Silico Model for Predicting Drug-Induced Hepatotoxicity. Int J Mol Sci 2019;20:E1897. [PMID: 30999595 DOI: 10.3390/ijms20081897] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
5 Komura H, Watanabe R, Kawashima H, Ohashi R, Kuroda M, Sato T, Honma T, Mizuguchi K. A public-private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties. Drug Discov Today 2021;26:1275-83. [PMID: 33516857 DOI: 10.1016/j.drudis.2021.01.024] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 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]
7 Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. Biotechnol Bioprocess Eng 2020;25:895-930. [PMID: 33437151 DOI: 10.1007/s12257-020-0049-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
8 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
9 He S, Zhang C, Zhou P, Zhang X, Ye T, Wang R, Sun G, Sun X. Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis. Int J Mol Sci 2019;20:E3633. [PMID: 31349548 DOI: 10.3390/ijms20153633] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 5.3] [Reference Citation Analysis]
10 Ancuceanu R, Hovanet MV, Anghel AI, Furtunescu F, Neagu M, Constantin C, Dinu M. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. Int J Mol Sci 2020;21:E2114. [PMID: 32204453 DOI: 10.3390/ijms21062114] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
11 Nguyen-Vo TH, Nguyen L, Do N, Le PH, Nguyen TN, Nguyen BP, Le L. Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features. ACS Omega 2020;5:25432-9. [PMID: 33043223 DOI: 10.1021/acsomega.0c03866] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
12 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]
13 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]
14 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
15 Kang MG, Kang NS. Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space. Molecules 2021;26:7548. [PMID: 34946636 DOI: 10.3390/molecules26247548] [Reference Citation Analysis]
16 Rathman J, Yang C, Ribeiro JV, Mostrag A, Thakkar S, Tong W, Hobocienski B, Sacher O, Magdziarz T, Bienfait B. Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment. Chem Res Toxicol 2021;34:601-15. [PMID: 33356149 DOI: 10.1021/acs.chemrestox.0c00423] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Chen Z, Jiang Y, Zhang X, Zheng R, Qiu R, Sun Y, Zhao C, Shang H. ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. Brief Bioinform 2021:bbab503. [PMID: 34882224 DOI: 10.1093/bib/bbab503] [Reference Citation Analysis]
18 Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2021;34:217-39. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
19 Raschi E, Caraceni P, Poluzzi E, De Ponti F. Baricitinib, JAK inhibitors and liver injury: a cause for concern in COVID-19? Expert Opin Drug Saf 2020;19:1367-9. [PMID: 32840116 DOI: 10.1080/14740338.2020.1812191] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
20 He S, Zhang X, Lu S, Zhu T, Sun G, Sun X. A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study. Biomolecules 2019;9:E577. [PMID: 31591318 DOI: 10.3390/biom9100577] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
21 Liu L, Zhang L, Feng H, Li S, Liu M, Zhao J, Liu H. Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods. Chem Res Toxicol 2021;34:1456-67. [PMID: 34047182 DOI: 10.1021/acs.chemrestox.0c00343] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Liu A, Walter M, Wright P, Bartosik A, Dolciami D, Elbasir A, Yang H, Bender A. Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biol Direct 2021;16:6. [PMID: 33461600 DOI: 10.1186/s13062-020-00285-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
23 Ai H, Wu X, Zhang L, Qi M, Zhao Y, Zhao Q, Zhao J, Liu H. QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods. Ecotoxicol Environ Saf 2019;179:71-8. [PMID: 31026752 DOI: 10.1016/j.ecoenv.2019.04.035] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
24 Liu Y, Gao H, He YD. A compound attributes-based predictive model for drug induced liver injury in humans. PLoS One 2020;15:e0231252. [PMID: 32294131 DOI: 10.1371/journal.pone.0231252] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
25 Vall A, Sabnis Y, Shi J, Class R, Hochreiter S, Klambauer G. The Promise of AI for DILI Prediction. Front Artif Intell 2021;4:638410. [PMID: 33937745 DOI: 10.3389/frai.2021.638410] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Minerali E, Foil DH, Zorn KM, Lane TR, Ekins S. Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI). Mol Pharm 2020;17:2628-37. [PMID: 32422053 DOI: 10.1021/acs.molpharmaceut.0c00326] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
27 Li T, Tong W, Roberts R, Liu Z, Thakkar S. DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation. Chem Res Toxicol 2021;34:550-65. [PMID: 33356151 DOI: 10.1021/acs.chemrestox.0c00374] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]