ArticleBMC bioinformatics2018
Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of disease clusters in multimorbidity.Nature communications · 2025Pooled it
- Characterizing co-purchased food products with soda, fresh fruits, and fresh vegetables using loyalty card purchasing data in Montréal, Canada, 2015-2017.The international journal of behavioral nutrition and physical activity · 2025Article
- The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci.Nature computational science · 2023Article
- Mining comorbidities of opioid use disorder from FDA adverse event reporting system and patient electronic health records.BMC medical informatics and decision making · 2022Article
- Known allosteric proteins have central roles in genetic disease.PLoS computational biology · 2022Article
- Extracting Significant Comorbid Diseases from MeSH Index of PubMed.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing.BMC medical genomics · 2021Article
- Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19.Molecular neurobiology · 2021Article
- Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System.Frontiers in pharmacology · 2021Article
- Decoding information on COVID-19: Ontological approach towards design possible therapeutics.Informatics in medicine unlocked · 2021Review
- Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery.Bioinformatics (Oxford, England) · 2020Article
- Analysis of disease organ as a novel phenotype towards disease genetics understanding.Journal of biomedical informatics · 2019Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
2 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.