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Papers
61,005 resultsShowing papers similar to Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis
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.
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.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Combining network toxicology, machine learning, and molecular docking, this study found that PET plastic degradation products ethylene glycol and terephthalic acid may influence colorectal cancer prognosis through 43 shared genes linked to TNF/IL-17 signaling and glucocorticoid-mediated metabolic pathways.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Researchers used network toxicology, machine learning, and molecular docking to investigate how PET degradation products—ethylene glycol and terephthalic acid—affect colorectal cancer prognosis through the glucocorticoid signaling pathway. The analysis identified 43 shared target genes, suggesting that PET breakdown products may worsen colorectal cancer outcomes by dysregulating glucocorticoid-mediated anti-inflammatory and cell survival signals.
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.
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.
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.
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
Researchers used machine learning to predict the toxicity of five common microplastic types on human lung cells, finding that particle size, plastic type, and exposure concentration were the most important factors determining harm. This computational approach could help assess the health risks of different microplastics more efficiently than traditional lab testing alone.
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.
Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment
Researchers developed machine learning models to predict dynamic changes in pathogenic Vibrio bacteria abundance on microplastics in marine environments, finding that environmental factors like temperature and salinity significantly influence pathogen colonization on plastic surfaces.
Rank-In Integrated Machine Learning and Bioinformatic Analysis Identified the Key Genes in HFPO-DA (GenX) Exposure to Human, Mouse, and Rat Organisms
Researchers used integrated machine learning and bioinformatic analysis to identify key molecular markers and pathways associated with microplastic-induced biological effects, generating mechanistic hypotheses for further experimental validation.
Prediction of the cytotoxicity of micro- and nanoplastics using machine learning combined with literature data mining
Researchers developed a machine learning framework using decision tree ensemble classifiers trained on 1,824 literature-derived data points to predict the cytotoxicity of micro- and nanoplastics based on nine physicochemical and experimental features. The full-feature model achieved 95% accuracy with 86% recall and precision, and feature selection identified six key predictors, providing a tool to guide experimental design and harmonize MNP toxicity research.
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.
AI-Enhanced Patient-Derived Cancer Organoids: Integrating Machine Learning for Precision Oncology
This review explores how combining patient-derived cancer organoids with artificial intelligence enables more precise drug sensitivity predictions and biomarker discovery in oncology research. While not directly related to microplastic research, the study demonstrates how AI and advanced biological models can be integrated to analyze complex datasets. The approaches described may inform future methods for studying how environmental contaminants interact with human tissues.
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.
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 identify which characteristics of micro and nanoplastics are most toxic to intestinal cells. The researchers found that particle size, shape, and concentration all play important roles in how much damage these plastics cause to gut lining cells, helping us understand how ingested microplastics might affect digestive health.
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.
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.
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.
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.
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.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
LASSO regression screening of key targets and their internal validation analysis.
This study used LASSO regression to identify three key gene targets through which microplastics may contribute to allergic rhinitis (AR), validated through ROC curves and single-gene GSEA analysis. The results reveal differential expression profiles and enrichment pathways for these targets, providing potential diagnostic biomarkers linking microplastic exposure to AR pathogenesis.