MODCAN: driver gene identification based on multi-omics features and differential co-association networks for tumor subtypes

When applied to ten cancer datasets from TCGA, MODCAN significantly outperforms both existing supervised and unsupervised learning algorithms, exhibiting superior performance in terms of precision, recall, and AUPR. Furthermore, MODCAN demonstrates substantial advantages in predicting potential tumor-specific driver genes. Notably, these genes not only exhibit strong specificity for their respective cancers, but also reveal tumor heterogeneity across distinct subtypes.