AIMC Topic: Carcinogens
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Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.
International journal of molecular sciences
Sep 29, 2022
Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocar...
Toxicity, genotoxicity, and carcinogenicity of 2-methylfuran in a 90-day comprehensive toxicity study in gpt delta rats.
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Aug 13, 2022
2-Methylfuran (2-MF) exists naturally in foods and is used as a flavoring agent. Furan, the core structure of 2-MF, possesses hepatocarcinogenicity in rodents. Accumulation of toxicological information on furan derivatives is needed to elucidate thei...
Artificial intelligence uncovers carcinogenic human metabolites.
Nature chemical biology
Aug 11, 2022
The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger m...
A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods.
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Jan 1, 2022
Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental m...
Deep neural network for the determination of transformed foci in Bhas 42 cell transformation assay.
Scientific reports
Dec 2, 2021
Bhas 42 cell transformation assay (CTA) has been used to estimate the carcinogenic potential of chemicals by exposing Bhas 42 cells to carcinogenic stimuli to form colonies, referred to as transformed foci, on the confluent monolayer. Transformed foc...
Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies.
Toxicologic pathology
Dec 8, 2020
In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test study validity based on the presence of the expected proliferative lesions in the transgenic mice. We h...
CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens.
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Oct 25, 2019
Determining chemical carcinogenicity in the early stages of drug discovery is fundamentally important to prevent the adverse effect of carcinogens on human health. There has been a recent surge of interest in developing computational approaches to pr...
Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints.
Journal of applied toxicology : JAT
Feb 14, 2019
The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several...
Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction.
Regulatory toxicology and pharmacology : RTP
Jan 11, 2018
In vitro genotoxicity bioassays are cost-efficient methods of assessing potential carcinogens. However, many genotoxicity bioassays are inappropriate for detecting chemicals eliciting non-genotoxic mechanisms, such as tumour promotion, this necessita...
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.
Scientific reports
May 18, 2017
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBo...
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