ArticleScientific reports2018
Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Safe and sustainable by design (SSbD) for nanotechnology: scientific foundations, global policy perspectives, and industrial practice.Nano convergence · 2026Review
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- NanoToxRadar: A Multitarget Nano-QSAR Model for Predicting the Cytotoxicity Values of Multicomponent Nanoparticles.ACS nanoscience Au · 2025Article
- Nanofibrous Polymer Filters for Removal of Metal Oxide Nanoparticles from Industrial Processes.Membranes · 2025Article
- Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.ACS nano · 2025Article
- Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning.Research square · 2025Article
- AI-based nanotoxicity data extraction and prediction of nanotoxicity.Computational and structural biotechnology journal · 2025Article
- Synergestic Protein-Green synthesized Nanoparticles Nanosystems: A Sustainable and Safe Approach for Cancer Theranostics.Nanotheranostics · 2025Review
- In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.Computational and structural biotechnology journal · 2024Article
- Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges.Environment & health (Washington, D.C.) · 2024Review
- A review on the structural characterization of nanomaterials for nano-QSAR models.Beilstein journal of nanotechnology · 2024Review
- Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools.Journal of cheminformatics · 2023Article
- In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES.International journal of molecular sciences · 2023Article
- Review
- Merging data curation and machine learning to improve nanomedicines.Advanced drug delivery reviews · 2022Review
- The toxicity of SiOJournal of environmental health science & engineering · 2021Article
- NanoTox: Development of a ParsimoniousACS omega · 2021Article
- Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity.Nanomaterials (Basel, Switzerland) · 2021Review
- Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging.Frontiers in immunology · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.