ArticleInternational journal of molecular sciences2020
Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- Review
- Application of 3D Cell Culture Techniques in Nanotoxicology: How Far Are We?Stem cell reviews and reports · 2026Review
- Review
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Automated Large-Scale Extraction of Nanotoxicity Data from the Literature Using Multimodal Large Language Models.Computational and structural biotechnology journal · 2026Article
- Dynamic QSAR modeling for predicting in vivo genotoxicity and inflammation induced by nanoparticles and advanced materials: a time-dose-property/response approach.Journal of nanobiotechnology · 2025Article
- Article
- Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning.BMC biomedical engineering · 2024Article
- Article
- Review
- Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency.International journal of molecular sciences · 2023Review
- Mechanisms of Nanotoxicology and the Important Role of Alternative Testing Strategies.International journal of molecular sciences · 2022Article
- Brain physiome: A concept bridgingBioactive materials · 2022Review
- Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment.Nanomaterials (Basel, Switzerland) · 2022Review
- Analysis of Nanotoxicity with Integrated Omics and Mechanobiology.Nanomaterials (Basel, Switzerland) · 2021Review
- Data Shepherding in Nanotechnology. The Exposure Field Campaign Template.Nanomaterials (Basel, Switzerland) · 2021Article
- A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles.Nanomaterials (Basel, Switzerland) · 2021Article
- NanoTox: Development of a ParsimoniousACS omega · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.
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Registered trials
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