Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 14, Issue 3, Pages 678-686Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2016.2550443
Keywords
Disease comorbidity; disease gene; disease similarity; pathway; protein-protein interaction
Categories
Funding
- National Science Foundation of China [61520106006, 61572363, 91530321, 31571364, 61133010, 61532008, 61572364, 61373105, 61303111, 61411140249, 61402334]
- National High-Tech RD Program (863) [2014AA021502, 2015AA020101]
- Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund
- Fundamental Research Funds for the Central Universities
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Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.
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