ArticleMolecules (Basel, Switzerland)2020
QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors.
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- New approach methodologies to facilitate and improve the hazard assessment of non-genotoxic carcinogens-a PARC project.Frontiers in toxicology · 2023Review
- Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.Sensors (Basel, Switzerland) · 2022Article
- Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.Molecules (Basel, Switzerland) · 2021Article
- Preoperative prediction of lymph node metastasis in patients with papillary thyroid carcinoma by an artificial intelligence algorithm.American journal of translational research · 2021Article
- QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors.Molecules (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure-activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r
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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.