Deep Learning-Based Prediction of Lung Cancer Driver Genes

Cancer driver genes play a key role in tumor development, and the mutation or aberrant expression of these genes, which mainly occurs at the somatic cell level, has become a focus of research in recent years. Identifying lung cancer driver genes from millions of somatic cell mutations is undoubtedly a challenging task. In order to accurately predict lung cancer driver genes, researchers have proposed numerous computational methods, most of which employ machine learning techniques such as random forests and support vector machines (SVMs). However, simply merging features extracted from different data sources and using the selected features for training machine learning models is not an optimal strategy. New research points out that gene expression data can effectively characterize the similarity between genes. Based on this finding, an advanced deep learning-based analysis method, GSNDriver is proposed which significantly improves the prediction accuracy of lung cancer driver genes by probing deeply into the mutational features of genes and their neighbors through Transformer networks. This method performs well in lung cancer research, especially in the prediction of lung adenocarcinoma and lung squamous carcinoma, and achieves better performance in AUC performance, precision and recall, achieving the performance of 0.981, 0.985, and 0.807, and 0.983, 0.991, and 0.725, respectively, which is significantly better than other algorithms. The proposed method GSNDriver provides an innovative way to identify new lung cancer driver genes. Through in-depth analysis of lung cancer-related genomic data, GSNDriver is able to accurately identify potential oncogenes and reveal their key roles in lung cancer development.