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RESEARCH PAPER · Journal of Hazardous Materials · 2022

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Xiaotong Wu, Qixing Zhou, Mu Li, Xiangang Hu

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Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectivesOriginalModeling and insights into the structural characteristics of drug-induced autoimmune diseases18Identification of Cyanobacterial Estrogen-Like Compounds by Metabolomics and Machine Learning1Computational Exploration of Bio-Degradation Patterns of Various Plastic Types19Addressing chemical pollution in biodiversity research206Digital mapping of soil organic carbon density in China using an ensemble model55Mapeo geográfico toxicológico de plaguicidas utilizados en cultivos de maíz zona norte de la provincia de Los Ríos1Advancing Computational Toxicology by Interpretable Machine Learning183A Benchmark Dataset for Machine Learning in Ecotoxicology1Effects of heavy metal exposure on hypertension: A machine learning modeling approach66Advances and applications of machine learning and deep learning in environmental ecology and health119Virtual chemical analysis and machine learning-based prediction of polyethylene terephthalate nanoplastics toxicity on aquatic organisms as influenced by particle size and properties2A benchmark dataset for machine learning in ecotoxicology68A Comparative Review: Research in Safety and Sustainability of Carbon Nanomaterials Without and With Machine Learning Assistance11Emerging Technology and Future Directions in Environmental Nanotoxicology3Pollution distribution characteristics and ecological risks of typical emerging chemical contaminants in aquatic environments1Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split7Exploring pollutant joint effects in disease through interpretable machine learning16Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective19Predicting thermal desorption efficiency of PAHs in contaminated sites based on an optimized machine learning approach16Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay6Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens37Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matter22Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis15Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions34Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish15Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective32Transferring knowledge across aquatic species via clustering techniques to unravel patterns of pesticide toxicity4A new approach for predicting oil mobilities and unveiling their controlling factors in a lacustrine shale system: Insights from interpretable machine learning model41A Multiscale-Information-Embedded Universal Toxicity Prediction Framework1A systematic review of current AI techniques used in the context of the SDGs53Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: A machine learning approach9MOHGCN: A trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis14Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics14A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology18Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation2Comparison of Classification and Regression Models for Predicting Tissue-Residue Toxicity of Legacy and Novel Brominated Flame Retardants in Threatened and Endangered Fish Species0Exploring Microbial Response Indicators to Environmental Disturbances in Freshwater Ecosystems0Toxicity Prediction and Risk Assessment of Industrial And Warfare Chemicals Using Machine Learning-Enhanced Qsar0Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability23Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model9Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics12Marine Heatwaves and Iceberg Melting in Polar Areas Intensify Phytoplankton Blooms9Unraveling the complexity of organophosphorus pesticides: Ecological risks, biochemical pathways and the promise of machine learning16Revisiting the approaches to DNA damage detection in genetic toxicology: insights and regulatory implications11Machine learning-optimized advanced oxidation for enhanced sludge dewatering: EPS mechanistic insights and predictive modeling33Application of Bioinformatics and Machine Learning Tools in Food Safety4Hierarchical Federated Learning with Hybrid Neural Architectures for Predictive Pollutant Analysis in Advanced Green Analytical Chemistry3Experimental study and machine learning prediction on compressive strength of industrial waste- solidified marine soft soil under dry-wet cycles6The role of machine learning in predictive toxicology: A review of current trends and future perspectives54ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment18Part II. Systematic development of machine learning models for predicting human and ecotoxicity from in vivo OECD test guideline data3CBERTaXGB: A domain-specific transformer-XGBoost hybrid for chemical toxicity and flammability prediction3Part I. Systematic development of machine learning models for predicting mechanism-based toxicity from in vitro ToxCast bioassay data4AI-powered image standardization in microphysiological system platforms1Optimized Random Forest Regression for Predictive Modeling of Environmental Correlation Metrics in Typhoon Data0Integrating Experimental Toxicology and Machine Learning to Model Levonorgestrel-Induced Oxidative Damage in Zebrafish1Interpretable machine learning models for predicting hormesis of binary antibiotic mixtures on Vibrio qinghaiensis sp.-Q671Basics of machine learning and artificial intelligence methods in toxicology and environmental health1From data to decisions: Leveraging machine learning and artificial intelligence methods for human health risk assessment of environmental pollutants0Multiscale-Information-Embedded Universal Toxicity Prediction Framework8Deep learning advances high-throughput toxicity screening of chemicals at multi-biological levels2Implementation of machine learning models bridges the prognostic gap in Aluminum Phosphide poisoning1Enhancing Hansen Solubility Predictions: A Combined Approach Using Integrated Datasets and Optimized Machine Learning Techniques1Large-scale chemical language models for accurate and interpretable prediction of reactivity and toxicity27Radical-Net: A chemistry-enhanced transformer for elementary radical reactions in pollutant chemistry23Network toxicology, machine learning combined with experimental validation: Rutin ameliorates NAFLD and prevents BHA-induced hepatotoxicity via Casp3 suppression1Big data integration for environmental risk assessment of emerging contaminants27Toward Sustainable Liquid Crystal Displays: A Green Prioritization Framework for Liquid Crystal Monomers Integrating Risk Assessment, Mechanistic Insights, and Regulatory Strategies3Artificial intelligence-driven environmental toxicology: Emerging strategies for microplastics risk assessment4Integrating Single‐Cell Sequencing, Machine Learning, and Molecular Docking to Elucidate the Molecular Network Linking Myocardial Infarction and DEHP Exposure1Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity0Enhancing Water Resources Management: The Transformative Role of Artificial Intelligence in Modeling Trace and Emerging Contaminants1Some Degree‐Based Topological Indices Associated With Line Graphs of Backbone Deoxyribonucleic Acid Networks: A Polynomial Function Approximation0Reflective diagnostics: A self-learning CRISPR–biosensor–AI platform for adaptive food and health safety monitoring1In Silico Hazard Assessment of Ototoxicants Through Machine Learning and Computational Systems Biology0Using Machine Learning to Predict First-Order Reaction Rate Constants of PFAS Degradation3Predicting ecological risks of heavy metals in watersheds based on interpretable machine learning models: under the framework of data scarcity1Recent Advances in Deep Learning-Based Toxicity Prediction of Emerging Organic Pollutants in Freshwater Ecosystems1Advancing metal organic frameworks screening and remediation condition optimization for removing persistent organic pollutant via integrated machine learning and theoretical calculation insights7A decision-support framework for early-stage screening of sustainable and degradable plastics in a circular economy1Framing Effects in Personalized In-Vehicle Freeway Traffic Messaging: A HUD-Based Approach0Comparative Evaluation of Deep Learning Graph Neural Networks and Classical Machine Learning Models for Predicting Heats of Combustion as a Key Hazard Indicator under GHS/CLP Standards0Predictive Efficacy of Classification and Regression Models for Tissue-Residue Toxicity of Brominated Flame Retardants in Different Fish Species0MACHINE LEARNING APPLICATIONS IN TOXICOLOGY: CURRENT APPROACHES AND FUTURE PERSPECTIVES0Next generation risk assessment and new approach methodologies for sustainable management of heavy metal contaminated environments: A critical review0Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures0ToxiSpecies: Task-Aware Meta-Learning for Cross-Species Modeling of Acute Chemical Toxicity under Distribution Shift0Integrative network toxicology and single-cell analysis implicates a potential TGFBR1-centered fibroblast remodeling axis in Benzo[a]pyrene-related atrial fibrillation0DeepHeptox: An Interpretable Deep Learning Model for Multi-Endpoint Hepatotoxicity Prediction of Chemical Compounds0Predicting hazardous Sb(III)/Sb(V) adsorption on biochar via an interpretable ML-DFT integrated BiMeSorb framework with experimental validation0Quantitative decoupling of source–pathway–receptor driving mechanisms for integrated soil risk via interpretable machine learning0Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment0An ensemble graph-transformer/descriptor-fusion model with uncertainty diagnostics for ternary class screening of Daphnia magna acute toxicity0Mechanism-Driven Risk Assessment of 1,3,6-Tribromocarbazole: An Integrated Strategy Implicates ESR1-Associated Cardiovascular Toxicity0Construction of Adverse Outcome Pathway (AOP) for Triphenyl Phosphate (TPHP)–Induced Autism Spectrum Disorder (ASD) and Risk Identification of Neurodevelopmental Toxicity of Aryl Organophosphorus Flame Retardants (OPFRs) Based on Multidimensional Bioinformatic Data0

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