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MOTIVATION The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multidimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes (DEGs), variants in somatic mutations, and a gene interaction network (GIN), we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network, and then extracts the driver genes and correlated pathways using the minimum cover subset method. RESULTS Using other credible sources (e.g., Cancer Gene Census (CGC) and Network of Cancer Genes (NCG)), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation. AVAILABILITY The supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.