Affiliation:
1. School of Computer Science and Engineering, Central South University, Changsha, 410075, China
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.
Funder
Natural Science Foundation of Hunan Province
National Natural Science Foundation of China
Publisher
Bentham Science Publishers Ltd.
Subject
Genetics(clinical),Drug Discovery,Genetics,Molecular Biology,Molecular Medicine
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献