ReviewNanomaterials (Basel, Switzerland)2020
Practices and Trends of Machine Learning Application in Nanotoxicology.
Review in Nanomaterials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Safe and sustainable by design (SSbD) for nanotechnology: scientific foundations, global policy perspectives, and industrial practice.Nano convergence · 2026Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Comprehensive scientific insights on plant inspired nano therapeutics as the missing piece of the breast cancer puzzle.Discover nano · 2026Review
- Nanoscale biointerfaces in inter-organelle communication: membrane contact sites, organelle trafficking, and cell fate control.Frontiers in cell and developmental biology · 2026Review
- Automated Large-Scale Extraction of Nanotoxicity Data from the Literature Using Multimodal Large Language Models.Computational and structural biotechnology journal · 2026Article
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Nanotoxicology: developments and new insights.Nanomedicine (London, England) · 2025Review
- AI-based nanotoxicity data extraction and prediction of nanotoxicity.Computational and structural biotechnology journal · 2025Article
- 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
- Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application.Computational and structural biotechnology journal · 2024Article
- Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges.Environment & health (Washington, D.C.) · 2024Review
- Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials.Nanomaterials (Basel, Switzerland) · 2024Review
- A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.Particle and fibre toxicology · 2023Article
- Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools.Journal of cheminformatics · 2023Article
- Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms.ACS applied nano materials · 2023Article
- Experimental Methods for the Biological Evaluation of Nanoparticle-Based Drug Delivery Risks.Pharmaceutics · 2023Review
- Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency.International journal of molecular sciences · 2023Review
- Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives.Nanomaterials (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
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What OpenQuestion holds
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.