ISN: Inferring disease-related genes using seed gene and network analysis

In biology, text-mining is widely used to extract relationships between biological entities. Gene prioritization is also important to analyze diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using seed genes and network analysis. We constructed an integrating gene network for lung cancer by combining local gene networks for seed genes. Analyzing the integrating gene network, we inferred meaningful lung cancer-related genes and potential candidate genes. We also demonstrated that our method is more useful for extracting disease-gene relationships than previous methods. In this study, we extracted 21 lung cancer related genes and 11 candidate genes with supporting evidence of their association with lung cancer.