The results of Human Genome Project promote the development of bioinformatics. Searching disease genes that have function correlations, also called similar phenotype genes, based on the strategy of disease phenome similarity becomes an emerging research topic due to its important research value and wide range of applications. However, in biomedical field, there is no previous work that applies computer methods to search similar phenotype genes via a network consists of "gene-disease-phenotype" relations. To fill the gap, in this study, a disease information network containing three heterogeneous nodes (i.e., gene, disease, and phenotype) is built by making use of a disease open database. In addition, an algorithm, called gSim-Miner, is designed for the search of similar phenotype genes via the disease information network. Pruning strategies based on the characteristics of disease phenotype data are proposed to improve the efficiency of gSim-Miner. Experiments on real-world data sets demonstrate that the disease information network is feasible, and gSim-Miner is effective, efficient and extensible.