140 related articles for article (PubMed ID: 38467999)
1. Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint.
Qi Q; Wang Z
Environ Sci Pollut Res Int; 2024 Apr; 31(17):25114-25128. PubMed ID: 38467999
[TBL] [Abstract][Full Text] [Related]
2. Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles.
Zhang F; Wang Z; Peijnenburg WJGM; Vijver MG
Environ Int; 2023 Jul; 177():108025. PubMed ID: 37329761
[TBL] [Abstract][Full Text] [Related]
3. Meta-analysis of engineered nanoparticles dynamic aggregation in freshwater-like systems using machine learning techniques.
Yalezo N; Musee N
J Environ Manage; 2023 Jul; 337():117739. PubMed ID: 36934506
[TBL] [Abstract][Full Text] [Related]
4. Toxicity prediction of nanoparticles using machine learning approaches.
Ahmadi M; Ayyoubzadeh SM; Ghorbani-Bidkorpeh F
Toxicology; 2024 Jan; 501():153697. PubMed ID: 38056590
[TBL] [Abstract][Full Text] [Related]
5. Metal- and metal/oxide-based engineered nanoparticles and nanostructures: a review on the applications, nanotoxicological effects, and risk control strategies.
Solano R; Patiño-Ruiz D; Tejeda-Benitez L; Herrera A
Environ Sci Pollut Res Int; 2021 Apr; 28(14):16962-16981. PubMed ID: 33638785
[TBL] [Abstract][Full Text] [Related]
6. Unveiling combined ecotoxicity: Interactions and impacts of engineered nanoparticles and PPCPs.
Li X; Li L; Tang L; Mei J; Fu J
Sci Total Environ; 2024 Apr; 921():170746. PubMed ID: 38342466
[TBL] [Abstract][Full Text] [Related]
7. Predicting nanotoxicity by an integrated machine learning and metabolomics approach.
Peng T; Wei C; Yu F; Xu J; Zhou Q; Shi T; Hu X
Environ Pollut; 2020 Dec; 267():115434. PubMed ID: 32841907
[TBL] [Abstract][Full Text] [Related]
8. In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning.
Krishnan R; Howard IS; Comber S; Jha AN
Sci Total Environ; 2023 Aug; 887():164072. PubMed ID: 37268134
[TBL] [Abstract][Full Text] [Related]
9. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.
Ebrahim OA; Derbew G
Sci Rep; 2023 May; 13(1):7779. PubMed ID: 37179444
[TBL] [Abstract][Full Text] [Related]
10. [Prediction of trends for fine-scale spread of
Gong YF; Luo ZW; Feng JX; Xue JB; Guo ZY; Jin YJ; Yu Q; Xia S; Lü S; Xu J; Li SZ
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2022 Jun; 34(3):241-251. PubMed ID: 35896487
[TBL] [Abstract][Full Text] [Related]
11. From lab to ecosystem: Understanding the ecological footprints of engineered nanoparticles.
Gomte SS; Jadhav PV; Jothi Prasath V R N; Agnihotri TG; Jain A
J Environ Sci Health C Toxicol Carcinog; 2024; 42(1):33-73. PubMed ID: 38063467
[TBL] [Abstract][Full Text] [Related]
12. In silico prediction of drug-induced developmental toxicity by using machine learning approaches.
Zhang H; Mao J; Qi HZ; Ding L
Mol Divers; 2020 Nov; 24(4):1281-1290. PubMed ID: 31486961
[TBL] [Abstract][Full Text] [Related]
13. Predictive pollen-based biome modeling using machine learning.
Sobol MK; Finkelstein SA
PLoS One; 2018; 13(8):e0202214. PubMed ID: 30138366
[TBL] [Abstract][Full Text] [Related]
14. Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials.
Zhang F; Wang Z; Peijnenburg WJGM; Vijver MG
Environ Sci Technol; 2022 Nov; 56(22):15238-15250. PubMed ID: 36196869
[TBL] [Abstract][Full Text] [Related]
15. Phytoremediation of engineered nanoparticles using aquatic plants: Mechanisms and practical feasibility.
Ebrahimbabaie P; Meeinkuirt W; Pichtel J
J Environ Sci (China); 2020 Jul; 93():151-163. PubMed ID: 32446451
[TBL] [Abstract][Full Text] [Related]
16. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.
Russo DP; Zorn KM; Clark AM; Zhu H; Ekins S
Mol Pharm; 2018 Oct; 15(10):4361-4370. PubMed ID: 30114914
[TBL] [Abstract][Full Text] [Related]
17. Cluster model incorporating heterogeneous dose distribution of partial parotid irradiation for radiotherapy induced xerostomia prediction with machine learning methods.
Chao M; El Naqa I; Bakst RL; Lo YC; Peñagarícano JA
Acta Oncol; 2022 Jul; 61(7):842-848. PubMed ID: 35527717
[TBL] [Abstract][Full Text] [Related]
18. Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years.
Shim JG; Ryu KH; Cho EA; Ahn JH; Kim HK; Lee YJ; Lee SH
Medicina (Kaunas); 2021 Nov; 57(11):. PubMed ID: 34833448
[No Abstract] [Full Text] [Related]
19. Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease.
Khalid H; Khan A; Zahid Khan M; Mehmood G; Shuaib Qureshi M
Comput Intell Neurosci; 2023; 2023():9266889. PubMed ID: 36959840
[TBL] [Abstract][Full Text] [Related]
20. Impact of water composition on association of Ag and CeO₂ nanoparticles with aquatic macrophyte Elodea canadensis.
Van Koetsem F; Xiao Y; Luo Z; Du Laing G
Environ Sci Pollut Res Int; 2016 Mar; 23(6):5277-87. PubMed ID: 26564182
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
