Tae‐Jin Oh

This paper introduces BIN-PU, a PU learning framework for predicting bacterial protein-compound interactions using only positive samples, showing improved performance over existing methods.

This paper integrates Kolmogorov-Arnold Networks (KANs) into deep learning models for EC number prediction, showing improved performance and introducing an interpretation method to identify relevant features.

This paper introduces PEPIC, an explainable deep learning model for classifying plastic-hydrolytic enzymes from protein sequences, showing improved performance and biological validation.

HIT-EC improves EC number prediction with a hierarchical transformer model, showing better performance and interpretability than existing methods.