Louis Dumontet and Mingon Kang (both Computer Science) have published a research article titled, “Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer" in Nature Communications. This study introduces a cutting-edge AI model that predicts enzyme functions from protein sequences and offers trustworthy prediction with domain-specific evidence. This is an international collaborative and interdisciplinary study.
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, the authors propose a hierarchical interpretable transformer model, HIT-EC, for trustworthy EC number prediction.
