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Research Article

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Author(s): Xuegong Chen, Wanwan Shi and Lei Deng*

Volume 19, Issue 4, 2019

Page: [232 - 241] Pages: 10

DOI: 10.2174/1566523219666190917155959

Price: $65

Abstract

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.

Keywords: Disease comorbidity, HeteSim measure, heterogeneous network, disease gene, disease drug, protein-protein interaction.

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