Mining algorithm for breast cancer candidate disease module based on key node groups

In order to solve the problems of small quantity, incomplete data, noise, and bias of the gene expression profile in the method for breast cancer disease module mining, a mining algorithm for candidate disease module based on the key node groups and the local node fitness constraints, the key node groups and local fitness (KNGLF) algorithm, is proposed. First, the topological overlap similarity score and the functional similarity score between the candidate genes and the pathogenic genes are fused into a fusion score. Through comparing the fusion score with the threshold value, the key nodes are selected and the key node groups are constructed. Then, the breast cancer candidate disease modules are mined based on the local fitness constraints and different decision criteria for different nodes. Finally, according to the enrichment analysis results, the candidate disease gene modules are identified. The experimental results show that compared with other existing mining algorithms for breast cancer module, the key node selection algorithm in the KNGLF algorithm has the smaller MRR (mean rank ratio) but the greater AUC (area under curve). Fifteen breast cancer candidate gene modules with significant biological significance are identified by the KNGLF algorithm. Besides, the KNGLF algorithm can be extended to identify other diseases related candidate modules.