Predicting Gene Relations with a Graph Transformer Network Integrating DNA, Protein, and Descriptive Data

Gene relation prediction is crucial for understanding cancer pathways and developing targeted treatments. This study proposes a novel Graph Transformer Network to predict gene relations by integrating DNA sequences, amino acids sequences, and gene descriptions. Utilizing the KEGG pathway database, our model aggregates diverse gene information to enhance gene regulatory pathway predictions. We encoded DNA sequences using DNA-BERT2, protein sequences using ESM2, and generated gene descriptions using ChatGPT, subsequently encoded by Bio-BERT. These embeddings were integrated and processed through a graph transformer with multi-head and single-head attention layers. Our results demonstrate that incorporating protein and description embeddings significantly improves predictive performance, while including DNA sequences shows marginal impact. The proposed model outperforms traditional machine learning and graph neural network-based methods, achieving an F-1 score of 0.893 on identifying whether two genes interact. This study highlights the potential of integrating multi-faceted genomic data to advance gene relation prediction, offering valuable insights for precision medicine and bioinformatics.