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For: Sizochenko N, Syzochenko M, Fjodorova N, Rasulev B, Leszczynski J. Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques. Ecotoxicology and Environmental Safety 2019;185:109733. [DOI: 10.1016/j.ecoenv.2019.109733] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Wang X, Li F, Teng Y, Ji C, Wu H. Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches. Sci Total Environ 2023;871:162103. [PMID: 36764549 DOI: 10.1016/j.scitotenv.2023.162103] [Reference Citation Analysis]
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3 Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. Sci Total Environ 2023;859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Reference Citation Analysis]
4 Zhou Y, Wang Y, Peijnenburg W, Vijver MG, Balraadjsing S, Fan W. Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms. Environ Sci Technol 2023. [PMID: 36730792 DOI: 10.1021/acs.est.2c07039] [Reference Citation Analysis]
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7 Chen X, Lv H. Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning. NPG Asia Mater 2022;14. [DOI: 10.1038/s41427-022-00416-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Balraadjsing S, Peijnenburg WJGM, Vijver MG. Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity. Chemosphere 2022;:135930. [PMID: 35961453 DOI: 10.1016/j.chemosphere.2022.135930] [Reference Citation Analysis]
9 Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. Ecotoxicol Environ Saf 2022;243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
10 Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives. Nanomaterials 2022;12:2646. [DOI: 10.3390/nano12152646] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Naguib M, Mekkawy IA, Mahmoud UM, Sayed AEH. Genotoxic evaluation of silver nanoparticles in catfish Clarias gariepinus erythrocytes; DNA strand breakage using comet assay. Scientific African 2022. [DOI: 10.1016/j.sciaf.2022.e01260] [Reference Citation Analysis]
12 Lv H, Chen X. Intelligent control of nanoparticle synthesis through machine learning. Nanoscale 2022;14:6688-708. [PMID: 35450983 DOI: 10.1039/d2nr00124a] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
13 Chen W, Hsu K, Fang T, Chen T, Li M. Characteristics and heterostructure of metal-doped TiO2/ZnO nanocatalysts. Current Applied Physics 2022. [DOI: 10.1016/j.cap.2022.03.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
15 Diéguez-santana K, Rasulev B, González-díaz H. Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks. Environ Sci : Nano 2022;9:1391-413. [DOI: 10.1039/d1en00967b] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Sun W, Zheng Y, Zhang Q, Yang K, Chen H, Cho Y, Fu J, Odunmbaku O, Shah AA, Xiao Z, Lu S, Chen S, Li M, Qin B, Yang C, Frauenheim T, Sun K. Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials. J Phys Chem Lett 2021;12:8847-54. [PMID: 34494851 DOI: 10.1021/acs.jpclett.1c02554] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
17 Cypriyana P J J, S S, Angalene J LA, Samrot AV, Kumar S S, Ponniah P, Chakravarthi S. Overview on toxicity of nanoparticles, it's mechanism, models used in toxicity studies and disposal methods – A review. Biocatalysis and Agricultural Biotechnology 2021;36:102117. [DOI: 10.1016/j.bcab.2021.102117] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
18 Rincón-López J, Almanza-Arjona YC, Riascos AP, Rojas-Aguirre Y. When Cyclodextrins Met Data Science: Unveiling Their Pharmaceutical Applications through Network Science and Text-Mining. Pharmaceutics 2021;13:1297. [PMID: 34452258 DOI: 10.3390/pharmaceutics13081297] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
19 Galvan D, Aquino A, Effting L, Mantovani ACG, Bona E, Conte-Junior CA. E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review. Crit Rev Food Sci Nutr 2021;:1-41. [PMID: 33779434 DOI: 10.1080/10408398.2021.1903384] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
20 Truc G, Rahmanian N, Pishnamazi M. Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems. Sustainability 2021;13:2527. [DOI: 10.3390/su13052527] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
21 Aslam MM, Du L, Ahmed Z, Irshad MN, Azeem H, Han L. A Deep Learning-Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network. Wireless Communications and Mobile Computing 2021;2021:1-16. [DOI: 10.1155/2021/7125482] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
22 Wong SW, Zhou G, Leung PT, Han J, Lee J, Kwok KW, Leung KM. Sunscreens containing zinc oxide nanoparticles can trigger oxidative stress and toxicity to the marine copepod Tigriopus japonicus. Marine Pollution Bulletin 2020;154:111078. [DOI: 10.1016/j.marpolbul.2020.111078] [Cited by in Crossref: 21] [Cited by in F6Publishing: 21] [Article Influence: 7.0] [Reference Citation Analysis]
23 Santiago EF, Pontes MS, Arruda GJ, Caires ARL, Colbeck I, Maldonado-rodriguez R, Grillo R. Understanding the Interaction of Nanopesticides with Plants. Nanopesticides 2020. [DOI: 10.1007/978-3-030-44873-8_4] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
24 Bonah E, Huang X, Aheto JH, Osae R. Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review. J Food Sci Technol 2020;57:1977-90. [PMID: 32431324 DOI: 10.1007/s13197-019-04143-4] [Cited by in Crossref: 23] [Cited by in F6Publishing: 30] [Article Influence: 5.8] [Reference Citation Analysis]