AI Medical Compendium Journal:
Nanotoxicology

Showing 1 to 5 of 5 articles

Bioinformatics and machine learning to support nanomaterial grouping.

Nanotoxicology
Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and ris...

Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish.

Nanotoxicology
The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present w...

Nanotoxicology data for tools: a literature review.

Nanotoxicology
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicolo...

Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory.

Nanotoxicology
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological ...

The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method.

Nanotoxicology
The regulatory agencies should fulfil the data gap in toxicity for new chemicals including nano-sized compounds, like metal oxides nanoparticles (MeO NPs) according to the registration, evaluation, authorisation and restriction of chemicals (REACH) l...