Predicting Driver Genes from Multi-Omics Data Using Hierarchical Multi-Feature Synergy Model

Cancer is an extremely complex disease, whose occurrence and development are influenced by a multitude of factors, among which the abnormal activity of cancer driver genes plays a crucial role in the pathological process. Identifying these genes allows researchers to understand pathogenic mechanisms and biological functions of cancer, facilitating the development of targeted therapies. Current methods for identifying driver genes often ignore the synergism among genes and the importance of features, thereby affecting identification accuracy. In this paper, we propose a cancer driver genes identification method called HMFS, which is based on the hierarchical multi-feature synergy model. Firstly, a hypergraph is constructed using Node2vec and K-means algorithm. By analyzing the topological feature and mutual exclusion degree of genes in each hyperedge, the Mutation Aggregation Coefficient is extracted. Then, based on the functional expression mechanism of genes, differential expression analysis is performed using miRNA and mRNA expression data. Finally, by analyzing the importance among features, the Hierarchical Multi-Feature Synergy is proposed for features fusion. In this paper, experiments are conducted on three real cancer datasets. Compared with seven representative methods, HMFS has the best performance on all evaluation indicators. HMFS source code can be obtained from https://github.com/DriverGene/HMFS.git.