Interpretable Kolmogorov-Arnold Networks for Enzyme Commission Number Prediction

Abstract

Accurate prediction of enzyme commission (EC) numbers remains a significant challenge in bioinformatics, limiting our ability to fully understand enzyme functions and their roles in biological processes. This paper presents the integration and evaluation of the next paradigm of deep learning architecture, named Kolmogorov-Arnold network (KAN), in state-of-the-art models for predicting EC numbers. KAN modules are incorporated into current state-of-the-art models to assess their impact on predictive performance. Additionally, we introduce a novel interpretation method, specifically designed for KANs, to identify relevant input features for a given prediction, addressing a current limitation in KANs. Our evaluation demonstrates that the integration of KANs significantly enhances predictive performance compared to the state-of-the-art deep learning models, with up to a 4.35% increase in micro-averaged F1 score and a 4.1% increase in macro-averaged F1 score. Moreover, our novel interpretation method not only enhances the predictions trustworthiness but also facilitates the discovery of motif sites within enzyme sequences. This innovative approach provides deeper insights into enzyme functionality and highlights potential new targets for research. The results underscore KANs effectiveness in improving enzymatic classification and advancing our understanding of enzyme structures and functions. The open-source code is publicly available at: https://github.com/datax-lab/kan_ecnumber.