GT-KGCN: Gene Essentiality Prediction with Graph Transformer and Kolmogorov-Arnold Network

Identifying essential genes in an organism is crucial for bioinformatics and genomics research. In this paper, we propose a new computational method for gene similarity prediction, named GT-KGCN, which combines the advantages of the Graph Transformer (GT), Graph Convolutional Network (GCN), and Kolmogorov-Arnold Network (KAN). Specifically, GT-KGCN utilizes graph transformers for feature embedding and multi-head attention aggregation, GCN for hierarchical aggregation of information from neighboring nodes, and KAN for optimizing feature learning through learnable activation functions. Model inputs include Protein-Protein Interaction (PPI) networks, gene expression profiles, subcellular localization, and orthologs information. We evaluated the performance of GT-KGCN on two different PPI network databases (BioGRID and STRING) using multi-omics datasets of Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. The experimental results show that GT-KGCN achieves a prediction accuracy of 99.12% for Escherichia coli, 98.47% for Saccharomyces cerevisiae, 92.22% for Drosophila melanogaster, and 92.30% for Homo sapiens. The GT-KGCN model outperforms traditional shallow machine learning methods and other deep learning methods in essential gene prediction. The ablation study verifies the positive effects of multi-omics datasets as well as different convolutional layers on the prediction performance of the model, with gene expression data showing the most significant performance improvement and demonstrating the model’s superiority.