XBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides.

ABSTRACT: Bitter peptides (BPs), derived from the hydrolysis of proteins in food, play a crucial role in both food science and biomedicine by influencing taste perception and participating in various physiological processes. Accurate identification of BPs is essential for understanding food quality and potential health impacts. Traditional machine learning approaches for BP identification have relied on conventional feature descriptors, achieving moderate success but struggling with the complexities of biological sequence data. Recent advances utilizing protein language model embedding and meta-learning approaches have improved the accuracy, but frequently neglect the molecular representations of peptides and lack interpretability. In this study, we propose xBitterT5, a novel multimodal and interpre

SUBMITTER: Nguyen NDH 

PROVIDER: S-EPMC12366191 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature