A Local–Global Graph KAN for Multi‐Class Prediction of PPI

Abstract

ABSTRACT Traditional experimental methods for identifying protein–protein interactions (PPI) are expensive and time‐consuming. Therefore, using machine learning to treat multiple PPI predictions as binary classifications has become an alternative, but there is a problem of data imbalance. The proposed GLGKAN‐PPI method integrates features from both global graphs and local subgraphs to capture the complex structural information of PPI networks comprehensively. Specifically, the method utilizes the pre‐trained model MASSA to extract multimodal features of proteins. The global graph features are extracted using the GKAN (Graph Kolmogorov‐Arnold Network) algorithm. Meanwhile, the local subgraph features are extracted using the MOE‐GKAN (Mixture of Experts‐Graph Kolmogorov‐Arnold Network) algorithm. To mitigate data imbalance, an asymmetric loss function is utilized to better handle minority classes and improve overall prediction accuracy. Experimental results demonstrate that GLGKAN‐PPI outperforms a range of existing intelligent approaches across multiple datasets and partitioning strategies.

Keywords

• asymmetric loss function
• global graphs and local subgraphs
• Graph Kolmogorov‐Arnold Network
• protein–protein interactions