Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer.

ABSTRACT: Accurate and trustworthy prediction of Enzyme Commission (EC) numbers is critical for understanding enzyme functions and their roles in biological processes. Despite the success of recently proposed deep learning-based models, there remain limitations, such as low performance in underrepresented EC numbers, lack of learning strategy with incomplete annotations, and limited interpretability. To address these challenges, we propose a hierarchical interpretable transformer model, HIT-EC, for trustworthy EC number prediction. HIT-EC employs a four-level transformer architecture that aligns with the hierarchical structure of EC numbers, and leverages both local and global dependencies within protein sequences for this multi-label classification task. We also propose a learning strategy to handl

SUBMITTER: Dumontet L 

PROVIDER: S-EPMC12858967 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature