Utilizing Multi-Modal Data Fusion for Predictive Risk Assessment of Endocrine Disruption by Microplastics in *Daphnia magna*

**Abstract:** Existing methods for assessing the endocrine-disrupting potential (EDP) of microplastics (*MPs*) rely primarily on single-endpoint assays, often failing to capture synergistic effects and complex biological responses. This paper introduces a novel framework, **Microplastic Endocrine Risk Prediction Engine (MERPE)**, leveraging multi-modal data fusion – integrating transcriptomic, proteomic, and behavioral data from *Daphnia magna* exposed to environmentally relevant MP mixtures – to predict long-term EDP risk with greater accuracy and mechanistic understanding. MERPE utilizes a layered neural network architecture combined with Shapley-AHP weighting and a novel HyperScore to quantify and prioritize MP mixtures based on their predicted EDP potential. The system anticipates a 15% reduction in false positives compared to existing single-endpoint methods and offers a pathway toward faster, more reliable risk assessment. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*