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Link Prediction Approaches for Disease Networks

  • Francesco Folino
  • Clara Pizzuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7451)

Abstract

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.

Keywords

Link Prediction Common Neighbor Disease Network Comorbidity Relation Disease Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francesco Folino
    • 1
  • Clara Pizzuti
    • 1
  1. 1.National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR)RendeItaly

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