Background: Accumulating experimental studies have indicated that disease comorbidity
causes additional pain to patients and leads to the failure of standard treatments compared to patients
who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design
more efficient treatment strategies. However, only a few disease comorbidities have been discovered
in the clinic.
Objective: In this work, we propose PCHS, an effective computational method for predicting disease
Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different
disease pairs in the global heterogeneous network, which integrates six networks based on biological
information, including disease-disease associations, drug-drug interactions, protein-protein interactions
and associations among them. We built the prediction model using the Support Vector Machine
(SVM) based on the HeteSim scores.
Results and Conclusion: The results showed that PCHS performed significantly better than previous
state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore,
some of our predictions have been verified in literatures, indicating the effectiveness of our method.