DDA: A Novel Network-Based Scoring Method to Identify Disease–Disease Associations

Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease–disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein–protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease–disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.