Link Prediction Approaches for Disease Networks Information Technology in Bio- and Medical Informatics

In the last years link prediction in complex networks has attracted an ever increasing attention from the scientific community. In this paper we apply link prediction models to a very challenging scenario: predicting the onset of future diseases on the base of the current health status of patients. To this purpose, a comorbidity network where nodes are the diseases and edges represent the contemporarily presence of two illnesses in a patient, is built. Similarity metrics that measure the proximity of two nodes by considering only the network topology are applied, and a ranked list of scores is computed. The higher the link score, the more likely the relationship between the two diseases will emerge. Experimental results show that the proposed technique can reveal morbidities a patient could develop in the future.