ME-PFP: An Ensemble Learning Approach Fusing Multi-Source Features for Protein Function Prediction

Proteins, as essential components of living organisms, play a critical role in both drug discovery and disease mechanism research. Multiple empirical studies have shown that there is a significant correlation between protein function and drug targets with therapeutic potential. Therefore, how to accurately and efficiently predict protein function is an urgent issue that needs to be addressed. Existing research faces challenges such as insufficient utilization of protein data and low heterogeneous fusion performance. In this paper, we propose ME-PFP, a novel ensemble learning framework that integrates sequence representations from a protein language model, domain, and protein-protein interaction data to improve protein function prediction. To effectively capture and utilize heterogeneous features, we design three specialized attention-based feature extractors tailored to each data modality. These features are then fused through a dynamic weighting strategy to enable complementary information exchange between different modalities, thereby improving protein function prediction performance. Extensive experiments on benchmark data sets show that ME-PFP significantly outperforms sequence-based and multisource fusion models. Notably, it achieved an average improvement of 13.23% on the human data set and 11.11% on the yeast data set. The experimental results show that this study not only improves the accuracy of protein function prediction, but also promotes progress in the field of computational biology.