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20 resultsShowing papers similar to Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles
ClearThe Effects of Single and Combined Stressors on Daphnids—Enzyme Markers of Physiology and Metabolomics Validate the Impact of Pollution
Researchers used daphnids to assess the impact of eight chemicals individually and as a mixture, finding that composite mixtures significantly enhanced toxicity and that enzyme markers combined with metabolomics can sensitively detect pollution effects.
Ecological risks of combination of multiple pollutants at environmentally relevant concentrations: Insights from the changes in life history traits, gut microbiota, and transcriptomic responses in Daphnia magna
Researchers exposed Daphnia magna to a combination of 11 pollutants including microplastics, antibiotics, and heavy metals at environmentally relevant ng/L–μg/L concentrations and found significant reductions in heart rate, reproduction, and lifespan, plus gut microbiota and transcriptomic changes — effects that single-pollutant studies would not predict.
Multi-omics characterisation of Daphnia magna exposed to PFAS and microplastics: transcriptome and gut microbiome datasets
Researchers generated a multi-omics dataset from Daphnia magna exposed to environmentally relevant concentrations of PFOS, PFOA, and PET microplastics, integrating gut microbiome 16S rRNA profiling and whole-organism transcriptomes to enable systems-level investigation of host-microbiome interactions under complex contaminant stress.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Assessing comparable bioconcentration potentials for nanoparticles in aquatic organisms via combined utilization of machine learning and toxicokinetic models
Researchers developed an eXtreme Gradient Boosting-derived toxicokinetic (XGB-TK) model combining machine learning and toxicokinetic modelling to predict bioconcentration factors for a broad range of metallic and carbonaceous nanoparticles in aquatic organisms, addressing the scarcity of experimental data for estimating nanoparticle bioaccumulation potential.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Advances in aquatic toxicology for predicting effects of multiple pollutants on aquatic organisms
This review examines advances in aquatic toxicology for predicting how mixtures of contaminants — heavy metals, pesticides, pharmaceuticals, and microplastics — interact in aquatic organisms, highlighting computational modeling and mixture toxicity approaches as key tools for environmental risk assessment.
New insights on the effects of ionic liquid structural changes at the gene expression level: Molecular mechanisms of toxicity in Daphnia magna
Researchers used RNA sequencing to investigate how structural changes in ionic liquids (designer industrial solvents) affect toxicity mechanisms in Daphnia magna, finding that alkyl chain length drives toxicity severity and that all tested ionic liquids share common mechanisms targeting cell membranes, oxidative stress, and DNA damage — including the supposedly safer choline-based variant.
Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
An Effective Machine Learning Scheme to Analyze and Predict the Concentration of Persistent Pollutants in the Great Lakes
Scientists applied multiple machine learning methods to predict concentrations of persistent organic pollutants in the Great Lakes, finding that LSTM neural networks outperformed simpler models for these complex time-series patterns. Similar predictive modeling could track microplastic concentrations in large water bodies over time.
Prediction of the joint toxicity of microplastics and organic pollutants on algae based on machine learning
Researchers used machine learning models to predict the combined toxicity of microplastics and organic pollutants on algae, achieving high accuracy with gradient-boosted decision tree models. They found that microplastic concentration, particle size, and the hydrophobicity of organic pollutants were the most important factors influencing toxic effects. The study provides a computational framework that could help assess environmental risks from microplastic-pollutant mixtures more efficiently than traditional laboratory testing.
Integrated Genomic and Bioinformatics Approaches to Identify Molecular Links between Endocrine Disruptors and Adverse Outcomes
This review examines how whole transcriptome sequencing and genomics technologies can identify molecular links between endocrine-disrupting chemical exposures and adverse outcomes in aquatic organisms, terrestrial animals, and humans.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Metabolomic Studies for the Evaluation of Toxicity Induced by Environmental Toxicants on Model Organisms
This review described how environmental metabolomics — measuring small-molecule profiles in model organisms — can be used to assess the toxicity of environmental contaminants including microplastics, heavy metals, and pesticides, and highlighted key organisms, methods, and data analysis approaches.
Benchmark Dose Estimation from Transcriptomics Data for Methylimidazolium Ionic Liquid Hepatotoxicity: Implications for Health Risk Assessment of Green Solvents
Researchers used transcriptomics and benchmark dose modeling to assess the liver toxicity of the ionic liquid 1-octyl-3-methylimidazolium, which has been detected at high concentrations in soils. They identified hundreds of differentially expressed genes involved in inflammatory and metabolic pathways and established toxicity thresholds for health risk assessment. The study raises concerns about the safety of ionic liquids promoted as green solvents, given their potential environmental persistence.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Combined effect of microplastics and tire particles on Daphnia magna: Insights from physiological and transcriptomic responses
Researchers investigated the combined effects of microplastics and tire particles on the water flea Daphnia magna, finding that the mixture triggered significant oxidative stress at environmentally relevant concentrations. Transcriptomic analysis revealed upregulation of antioxidant and metabolic stress genes, while energy reserves like glycogen were affected. The study suggests that co-exposure to these common freshwater pollutants may pose greater ecological risks than either particle type alone.
A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues
This study used multiple machine learning algorithms to model heavy metal accumulation in turbot tissues based on water and sediment contamination data. While focused on heavy metals rather than microplastics, the approach illustrates how predictive modeling can support environmental pollution monitoring.
Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.