{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Meneses J"],"funding":["European Commission","European Regional Development Fund"],"pagination":["21"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10201760"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(1)"],"pubmed_abstract":["<h4>Background</h4>The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles.<h4>Results</h4>Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance"],"journal":["Particle and fibre toxicology"],"pubmed_title":["A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data."],"pmcid":["PMC10201760"],"funding_grant_id":["857253","NORTE-01-0145-FEDER-000047"],"pubmed_authors":["Toropova AP","Toropov AA","Gonzalez-Durruthy M","Fernandez-de-Gortari E","Alfaro-Moreno E","Meneses J"],"additional_accession":[]},"is_claimable":false,"name":"A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.","description":"<h4>Background</h4>The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles.<h4>Results</h4>Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-26T22:43:28.292Z","creation":"2025-04-06T17:19:07.087Z"},"accession":"S-EPMC10201760","cross_references":{"pubmed":["37211608"],"doi":["10.1186/s12989-023-00530-0"]}}