Multi-DeepProtGraphGO: Integrating GCN on PPI Networks With Sequence-Driven Convolutional Bi-LSTM and Attention for...

Researchers in bioinformatics use protein function prediction to classify proteins, study their roles in disease mechanisms. Many mechanisms have been proposed to address the significant gap between the rapid increase in identified proteins and the slower growth in annotated protein functions. While PPI network data can assist in protein function prediction, it is often overlooked. Moreover, existing approaches primarily rely on node2vec embeddings of PPI data, neglecting valuable information about the neighborhood relationships of proteins. In this paper, we proposed a novel multi-modal approach that leverages a Graph Convolutional Network (GCN) to analyze PPI network data and explore neighboring proteins. Additionally, we employ Multi-Head Self-Attention combined with a Convolutional Bi-LSTM on protein sequence data to enhance the prediction of protein functions. To evaluate the effectiveness of our method, we utilize benchmark datasets, specifically from homo sapiens, such as String database for PPI network information and UniprotKB for protein sequences, ensuring a comprehensive and robust analysis. Extensive experiments demonstrate that the proposed method, Multi-DeepProtGraphGO, achieves improvements of +18.28%, +4.56%, and +6.92% in terms of the $F_{\max}$ score for the BP, CC, and MF sub-ontologies, respectively, compared to the state-of-the-art method CrossPredGO.