Integration of Multi-Omics Data With Topology Adaptive Graph Convolutional Network for Cancer Driver Gene Identification

The increasing volume of high-throughput molecular data has presented substantial computational challenges in the identification of cancer driver genes. We introduce ATTAG, a framework based on topology-adaptive graph neural networks with an attention mechanism, aimed at predicting cancer driver genes by integrating multi-omics data with biomolecular networks. ATTAG creates three distinct gene networks derived from protein interactions, gene semantic similarities, and co-occurrence within signaling pathways. Graph convolutional networks (GCNs) are employed to generate low-dimensional embeddings from these networks and multi-omics data. These embeddings are then refined through topology-adaptive graph neural networks for the prediction of cancer driver genes in BLCA. When compared to state-of-the-art methods, ATTAG demonstrates exceptional performance in identifying cancer driver genes. The experimental results underscore its ability to accurately identify both well-known driver genes and novel candidate cancer genes.