Cancer is the second most common cause of death in the United States. It is a complex disease with environmental, genetic, and lifestyle factors influencing the likelihood of getting cancer and the development of any resulting tumor. Understanding the genetics of cancer is integral to developing novel patient-specific treatments. However, due to complexity, hundreds to thousands of tumors are required for sufficient power to identify the network of relationships among these genes. Animal models of cancer are commonly used to reduce cost and to control experimental variables allowing for more specific hypothesis testing. The Sleeping Beauty transposon mutagenesis system can be used to model cancer in mice. While the Sleeping Beauty mutagenesis system is an important tool in understanding cancer, it has specific computational needs. Experiments need to be analyzed in a fast, unbiased, and efficient manner. A computational method must also accurately model the system allowing for validation and interpretation. Here I present an updated Integration Analysis System and use this system to validate the assumptions present in forward genetic screens of cancer using the Sleeping Beauty. This system allows for rapid identification of cancer genes, but does not directly aid in understanding the relationship between the genes. Given the complexity of cancer, understanding the relationship between cancer genes is very difficult. I have created a connectedness network utilizing the STRING database to better derive an understanding of cancer genes. STRING is a database of known and predicted protein-protein interactions. The connectedness between pairs of genes is calculated using a network reliability metric. This database allows for increased power to detect known pathways when compared to STRING alone. Combining this connectivity network with the set of cancer genes identified by the Integration Analysis System is a strategy for rapid and efficient interpretation of the genetic results.