Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 212, P. 117529 - 117529

Published: Jan. 4, 2025

Language: Английский

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Machine learning-driven prediction of nitrate-N adsorption efficiency by Fe-modified biochar: Refined model tuning and identification of crucial features

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 107026 - 107026

Published: Jan. 22, 2025

Language: Английский

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An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates

Sci, Journal Year: 2024, Volume and Issue: 6(4), P. 81 - 81

Published: Dec. 6, 2024

Hepatitis C virus (HCV) infection affects over 71 million people worldwide, leading to severe liver diseases, including cirrhosis and hepatocellular carcinoma. The virus’s high mutation rate complicates current antiviral therapies by promoting drug resistance, emphasizing the need for novel therapeutics. Traditional high-throughput screening (HTS) methods are costly, time-consuming, prone false positives, underscoring necessity more efficient alternatives. Machine learning (ML), particularly quantitative structure–activity relationship (QSAR) modeling, offers a promising solution predicting compounds’ biological activity based on chemical structures. However, “black-box” nature of many ML models raises concerns about interpretability, which is critical understanding action mechanisms. To address this, we propose an explainable multi-model stacked classifier (MMSC) hepatitis candidates. Our approach combines random forests (RF), support vector machines (SVM), gradient boosting (GBM), k-nearest neighbors (KNN) using logistic regression meta-learner. Trained tested dataset 495 compounds targeting HCV NS3 protease, model achieved 94.95% accuracy, 97.40% precision, 96.77% F1-score. Using SHAP values, provided interpretability identifying key molecular descriptors influencing model’s predictions. This MMSC improves discovery, bridging gap between predictive performance while offering actionable insights researchers.

Language: Английский

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Integration of transcriptomics and metabolomics reveal cytotoxic mechanisms of Polyethylene terephthalate microplastics in BEAS-2B cells

Food and Chemical Toxicology, Journal Year: 2024, Volume and Issue: unknown, P. 115125 - 115125

Published: Nov. 1, 2024

Language: Английский

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