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Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Strict Leave-One-Polymer-Out Validation
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Scientists used AI to predict how toxic different types of microplastics are to human lung cells, based on their chemical makeup. This matters because not all plastics are equally harmful, and this approach could help researchers flag risky plastics faster, without needing to test every single one in the lab.
This dataset supports the study “Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Strict Leave-One-Polymer-Out Validation.” It contains experimental and derived data used to develop and evaluate machine-learning models for predicting the cytotoxicity of microplastics in BEAS-2B human bronchial epithelial cells. The dataset includes polymer–concentration observations, biological and physicochemical descriptors, and model-related data used for nested leave-one-polymer-out (LOPO) cross-validation and model interpretation. The dataset is intended to support reproducibility, methodological transparency, and further research on microplastic toxicity prediction using multi-omics and machine-learning approaches.
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Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Strict Leave-One-Polymer-Out Validation
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Scientists used AI to predict how toxic different types of microplastics are to human lung cells, testing whether the models could correctly judge plastics they'd never seen before. This matters because it could help researchers flag which microplastics might harm our lungs without needing to test every single type in a lab, speeding up safety research.
Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Leave-One-Polymer-Out Validation.
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Scientists used computer models to predict how toxic different microplastic types are to human lung cells, combining particle size, electrical charge, and biological data. Particle size turned out to be the strongest warning sign of harm, though the model needs testing on more plastic types before it can reliably guide safety regulations or waste policies.
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
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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.
Machine learning prediction of organic contaminant partitioning to microplastics: A volume-normalized cross-polymer framework
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Scientists built an AI tool that predicts which chemical pollutants are most likely to stick to microplastic particles, based on the plastic's properties and the chemical's structure — without needing to run a new lab test every time. This matters because microplastics can act like tiny sponges that soak up toxic chemicals from water and carry them into our food and bodies, so being able to quickly flag the highest-risk chemical-plastic combinations helps regulators prioritize which contaminants deserve the most attention.
Integrating structure and experimental data annotations with computational modeling framework for predicting micro-nanoplastics toxicities
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Scientists have created a computer-based method to predict how toxic tiny plastic particles (microplastics and nanoplastics) might be to our cells, without needing to run slow, expensive lab experiments for every single type of plastic. By teaching computer models to recognize patterns based on a plastic particle's shape and size, researchers can now screen many more plastic types for potential health risks than traditional testing allows. This matters because it could speed up our understanding of which plastics pose the greatest danger as they build up in our environment and bodies.
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