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Papers
61,005 resultsShowing papers similar to Rank-In Integrated Machine Learning and Bioinformatic Analysis Identified the Key Genes in HFPO-DA (GenX) Exposure to Human, Mouse, and Rat Organisms
ClearThe toxicological impact of PET-MPs exposure on atherosclerosis: insights from network toxicology, molecular docking, and machine learning
Researchers used network toxicology, molecular docking, and machine learning to identify how PET microplastics may promote atherosclerosis, narrowing 28 candidate targets to seven key genes and predicting interactions with atherosclerosis-relevant pathways including inflammation and lipid metabolism.
Environmental PET-microplastic exposure and risk of non-alcoholic fatty liver disease: An integrated computational toxicology and multi-omics study
Researchers used computational toxicology and machine learning to identify six key genes linking PET microplastic exposure to non-alcoholic fatty liver disease (NAFLD), with the model achieving high diagnostic accuracy and molecular docking suggesting that PET-derived chemicals may directly bind to proteins controlling liver fat metabolism.
Integrating Genomic and Proteomic Data Using Machine Learning for Plastic Biodegradation: A Systematic Review
This systematic review summarizes how machine learning and genomic data are being used to identify microbes and enzymes that can break down plastic waste. The research is significant for microplastic concerns because discovering more effective biological degradation pathways could provide a natural solution for reducing the microplastic pollution that accumulates in our environment and bodies.
A computational framework for multi-scale data fusion in assessing the associations between micro- and nanoplastics and human hepatotoxicity
Researchers developed a computational toxicology framework integrating multi-source data and network analysis to map associations between micro- and nanoplastics and hepatotoxicity, identifying key molecular pathways through which MNPs may damage the liver, offering a scalable alternative to traditional in vivo testing.
Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis
Researchers combined computational biology, machine learning, and laboratory experiments to explore how polyethylene terephthalate microplastics might contribute to periodontitis, a common gum disease. They identified key molecular targets and signaling pathways through which microplastics could promote gum tissue inflammation. The study provides the first evidence linking microplastic exposure to the biological mechanisms underlying periodontal disease.
Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach
Researchers used ToxCast high-throughput screening data and deep learning models to build adverse outcome pathway (AOP) models for common plastic additive chemicals, identifying molecular initiating events and potential health endpoints relevant to human and environmental microplastic toxicity.
Revealing factors influencing polymer degradation with rank-based machine learning
Researchers developed a machine learning platform using a ranking-based algorithm to predict and compare how easily different polymer materials biodegrade, integrating three different experimental datasets with varying conditions. Analysis revealed key molecular factors that control degradability, offering guidance for designing more environmentally friendly plastics.
What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing Factors
Researchers analyzed 229 blood and 227 fecal samples for microplastics using pyrolysis-GC-MS and applied machine learning to identify the strongest predictors of microplastic body burden. The model identified diet, packaging use, and indoor environment as key drivers of microplastic levels in human blood and feces, highlighting lifestyle factors as modifiable exposure determinants.
Genes of filter-feeding species as a potential toolkit for monitoring microplastic impacts
Researchers developed a genetic toolkit using candidate genes from filter-feeding marine species to monitor the biological impacts of microplastic exposure in natural environments. They identified six genes across nine species that show measurable expression changes when organisms encounter microplastics. The study offers a practical molecular approach for tracking how microplastic pollution is actually affecting wild marine populations.
Adverse outcome pathway networks of microplastic ecotoxicity to aquatic organisms: A critical review
Researchers used automated text-mining combined with multi-level ecotoxicological review to construct adverse outcome pathway networks for microplastic toxicity in aquatic organisms. They mapped how microplastics cause harm from initial tissue contact through molecular disturbances to higher-level biological effects in gills, gut, liver, gonads, and brain. The study found strong evidence for early-stage toxic mechanisms but identified critical knowledge gaps in understanding downstream biological consequences.
Harmfulness Score: A Data‐Driven Framework for Ranking Environmental Risks of Microplastics
Researchers analyzed over 104,000 scientific abstracts on micro- and nanoplastics using bibliometric tools and machine learning to create a data-driven framework for ranking environmental risks. The resulting Harmfulness Score ranked polystyrene and polyethylene as the highest-risk polymers based on their association with oxidative stress, cytotoxicity, and genotoxicity in the scientific literature.
Integrative network toxicology and molecular docking preliminarily explore the potential role of polystyrene microplastics in childhood obesity
Researchers used computational methods including network toxicology, machine learning, and molecular docking to explore how polystyrene microplastics might contribute to childhood obesity. They identified 40 overlapping genes between obesity-related and microplastic-affected pathways, concentrated in lipid metabolism and insulin signaling. The study suggests that polystyrene microplastics may act as environmental triggers capable of disrupting metabolic balance by interacting with key regulatory genes.
An In Vitro Assay to Quantify Effects of Micro- and Nano-Plastics on Human Gene Transcription
Researchers developed an in vitro assay to quantify how micro- and nano-plastics affect human gene transcription, demonstrating that internalized plastic particles can alter gene expression patterns in human cells, providing a standardized tool for assessing plastic particle toxicity.
Intersection of microplastic toxicity targets and differentially expressed genes in allergic rhinitis.
Network analysis identified a set of genes that are both targeted by common microplastics (PE, PP, PVC, PS) and differentially expressed in allergic rhinitis, providing a molecular framework for investigating how microplastic exposure may contribute to nasal allergy pathogenesis.
A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations
Researchers developed a machine learning method combined with biological pathway analysis to predict the toxic effects of chemical mixtures, including PFAS (forever chemicals), at different doses. The approach was validated with lab experiments and accurately identified how chemical mixtures cause harm at the cellular level. While focused on PFAS rather than microplastics, this tool is relevant because microplastics often carry chemical mixtures, and better methods to assess combined toxicity are needed to understand real-world health risks.
Multi-Omics Approach on the Ecotoxicological Assessment of Microplastics
This review examines the application of multi-omics approaches — including genomics, transcriptomics, proteomics, and metabolomics — to the ecotoxicological assessment of microplastics in living organisms. The authors synthesize how these integrated molecular tools are advancing understanding of the mechanistic pathways by which microplastics disrupt biological systems, offering a more comprehensive picture than single-endpoint toxicity studies.
In-silico pharmacological insights into the therapeutic potential of microRNAs for microplastic-associated cancers
Researchers systematically screened published literature to identify cancer-related genes altered by microplastic exposure, then computationally evaluated microRNAs with anticancer activity that could target those genes, finding potential miRNA-based therapeutic candidates across breast, gastric, and other microplastic-associated tumor types.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
Protein-protein network analysis.
This study presents a protein-protein interaction network and LASSO regression analysis identifying key molecular targets through which microplastics may act in allergic rhinitis, using STRING database clustering and Genemama functional enrichment. The analysis identified three key gene targets and constructed a microplastic-target-pathway network to elucidate potential mechanistic pathways of microplastic-associated disease.
Toxicity of nanoplastics: machine learning combined with meta-analysis
This meta-analysis combined data from multiple studies and used machine learning to assess nanoplastic toxicity in mice. The findings showed that nanoplastics cause a wide range of harmful effects across multiple body systems, with the severity depending on particle size, type, exposure route, and duration. These results suggest that the nanoplastics we encounter daily could have complex, varied effects on mammalian health.
Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
This systematic review uses machine learning to determine which properties of micro and nanoplastics drive toxicity in human intestinal cell models. The findings reveal that smaller particles and higher concentrations cause more cell damage, which is important for understanding how the microplastics we swallow in food and water might harm our gut lining.
Combining machine learning with meta-analysis for predicting cytotoxicity of micro- and nanoplastics
This meta-analysis used machine learning to predict how toxic different types of micro- and nanoplastics are to cells. By analyzing data from many studies, it identified that particle size, concentration, and exposure time are key factors determining toxicity — smaller particles and longer exposures tend to cause more cell damage.
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.