ArticleBMC medical informatics and decision making2019
Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The genetic and observational nexus between diabetes and arthritis: a national health survey and mendelian randomization analysis.Nutrition & diabetes · 2024Article
- A knowledge empowered explainable gene ontology fingerprint approach to improve gene functional explication and prediction.iScience · 2023Article
- Literature-based discovery approaches for evidence-based healthcare: a systematic review.Health and technology · 2021Review
- The International Conference on Intelligent Biology and Medicine 2018: Medical Informatics Thematic Track (MedicalInfo2018).BMC medical informatics and decision making · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundDisease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to understand the mechanisms of diseases, improve the prevention and treatment of diseases, and support the discovery of new drugs or new uses of existing drugs.
methodsIn this paper, we introduced a mathematical model to represent gene related diseases with a series of associated genes based on the overrepresentation of genes and diseases in PubMed literature. We also illustrated an efficient way to reveal the implicit connections between COPD and other diseases based on this model.
resultsWe applied this approach to analyze the relationships between Chronic Obstructive Pulmonary Disease (COPD) and other diseases under the Lung diseases branch in the Medical subject heading index system and detected 4 novel diseases relevant to COPD. As judged by domain experts, the F score of our approach is up to 77.6%.
conclusionsThe results demonstrate the effectiveness of the gene fingerprint model for diseases on the basis of medical literature.
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