Enzyme Classification via Semi-Supervised Functional ResidueLearning
This paper introduces SLEEC, a semi-supervised learning framework that leverages multiple sequence alignment-based data augmentation to achieve state-of-the-art enzyme classification performance while providing interpretable residue-level annotations and robustness to common sequence modifications.