ArticleFrontiers in medicine2026
Uncertainty-aware physics-informed meta-learning for predicting niosomal particle size across formulations and processing conditions.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate prediction of niosomal particle size across diverse formulation compositions and processing conditions remains challenging because experimental datasets are typically limited and heterogeneous. This study presents an uncertainty-aware physics-guided meta-learning (PGML) framework for predicting the mean particle size of niosomal formulations prepared using the thin-film hydration (TFH) method. The framework integrates molecular descriptors, formulation variables, and processing conditions within a shared meta-learning representation, while soft formulation-guided regularization is used to discourage physically implausible model behavior. A probabilistic Gaussian-process prediction component provides predictive uncertainty together with particle-size estimates. The framework was evaluated using 114 formulations collected from 17 published studies and an independent test partition. The final model achieved an RMSE of approximately 0.65, an MAE of 0.43, and an R
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