AIMC Topic: Carcinogens

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Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.

Journal of chemical information and modeling
A carcinogenicity assessment of possibly carcinogenic chemicals (International Agency for Research on Cancer: IARC class 2B) was conducted using a consensus framework constructed from three complementary machine learning models: BiLSTM with MACCS fin...

Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation.

The Science of the total environment
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial dis...

MMF-MCP: A Deep Transfer Learning Model Based on Multimodal Information Fusion for Molecular Feature Extraction and Carcinogenicity Prediction.

Journal of chemical information and modeling
Molecular carcinogenicity is a crucial factor in the development of cancer, and accurate prediction of it is vital for cancer prevention, treatment, and drug development. In recent years, deep learning has been applied to predict molecular carcinogen...

A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression.

Journal of hazardous materials
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by th...

Multi-omics reveals the polyethylene terephthalate carcinogenicity: Cancer progression and immune microenvironment.

Ecotoxicology and environmental safety
Polyethylene terephthalate (PET), a polymer widely used in consumer products, has recently been implicated in cancer progression, though its mechanistic underpinnings remain elusive. This multidisciplinary study systematically investigates PET's carc...

AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity.

Mutation research. Genetic toxicology and environmental mutagenesis
This study aimed to develop an in silico model for predicting human carcinogenicity using advanced deep learning techniques, specifically Graph Neural Networks (GNN), through a multitask learning (MTL) approach. The MTL framework leveraged auxiliary ...

Carcinogenic and non-carcinogenic risks caused by rice contamination with heavy metals and their effect on the prevalence of cardiovascular disease (Using machine learning).

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
INTRODUCTION: The safety and health of food products are essential in the food industry, and the risk of contamination from various contaminants must be evaluated. Exposure to HMs from the environment (especially food) causes various adverse effects ...

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
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effect...

Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies...

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

Archives of toxicology
The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocar...