Abstract
BackgroundSystems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. Here, we explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network.ResultsWe constructed a disease comorbidity network with 1059 disease nodes and 12,608 edges using association rule mining of FAERS (14,157 rules). We evaluated the performance of comorbidity mining from FAERS using known disease comorbidities of multiple sclerosis (MS), psoriasis and obesity that represent rare, moderate and common disease respectively. Comorbidities of MS, obesity and psoriasis obtained from our network achieved precisions of 58.6%, 73.7%, 56.2% and recalls 87.5%, 69.2% and 72.7% separately. We performed comparative analysis of the disease comorbidity network with disease semantic network, disease genetic network and disease treatment network. We showed that (1) disease comorbidity clusters exhibit significantly higher semantic similarity than random network (0.18 vs 0.10); (2) disease comorbidity clusters share significantly more genes (0.46 vs 0.06); and (3) disease comorbidity clusters share significantly more drugs (0.64 vs 0.17). Finally, we demonstrated that the disease comorbidity network has potential in uncovering novel disease relationships using asthma as a case study.ConclusionsOur study presented the first comprehensive attempt to build a disease comorbidity network from FDA Adverse Event Reporting System. This network shows well correlated with disease semantic similarity, disease genetics and disease treatment, which has great potential in disease genetics prediction and drug discovery.
Highlights
Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery
We demonstrated that disease comorbidity network accurately captures diseasedisease relationships published in the literature and has great potential in both disease genetics prediction and drug discovery
Datasets FDA Adverse Event Reporting System (FAERS) data was downloaded from US Food and Drug Administration (FDA) [15], which contains 17,305,542 case reports for indications from 2004 to 2017
Summary
Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. We explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network. Studying disease relationship is an important strategy for disease gene discovery [3, 4]. Discovery of shared genetics of psoriasis and multiple sclerosis led to dimethyl fumarate, an anti-psoriasis drug, to be used in treatment of relapsing-remitting multiple sclerosis [5]. Many drug repurposing strategies are based on disease relationship [6]
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