Xuegong Chen, Wanwan Shi and Lei Deng* Pages 232 - 241 ( 10 )
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 comorbidity.
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.
Disease comorbidity, HeteSim measure, heterogeneous network, disease gene, disease drug, protein-protein interaction.
School of Computer Science and Engineering, Central South University, Changsha, 410075, School of Computer Science and Engineering, Central South University, Changsha, 410075, School of Computer Science and Engineering, Central South University, Changsha, 410075