Systematic Characterization and Prediction of Tumor-associated Genes in Mouse using microRNA

Gene (microRNA) identification is a key step in understanding the cellular mechanisms. Compared with biological experiments, computational prediction of disease genes is cheaper and more effortless. In this study, we analyzed the properties of tumor-associated microRNA in mouse and found that tumor-associated genes display 8distinguishingfeatures when compared with genes not yet known to be involved in tumor. The features of tumor-associated genes tend to located at network center and interact with each other were found by analyze the network characteristics. In addition, the features of the tumor-associated genes tend to be involved in certain biological processes and show certain phenotypes also were found through enrichment analysis. Based on these features, a machine-learning algorithm SVM were developed to predict new tumor-associated genes in mouse. Using the machine-learning algorithm, 120 tumor-associated genes were predicted with a posterior probability more than 0.9. We verified the accuracy of the identification framework with the data set of tumor-associated genes, and the result shows that this method is feasible.