Large-Scale Discovery of Disease-Disease and Disease-Gene Associations
Genre
Journal ArticleDate
2016-08-31Author
Gligorijevic, DStojanovic, J
Djuric, N
Radosavljevic, V
Grbovic, M
Kulathinal, RJ
Obradovic, Z
Subject
AlgorithmsDatabases, Factual
Electronic Health Records
Genetic Diseases, Inborn
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Phenotype
Permanent link to this record
http://hdl.handle.net/20.500.12613/5028
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10.1038/srep32404Abstract
© 2016 The Author(s). Data-driven phenotype analyses on Electronic Health Record (EHR) data have recently drawn benefits across many areas of clinical practice, uncovering new links in the medical sciences that can potentially affect the well-being of millions of patients. In this paper, EHR data is used to discover novel relationships between diseases by studying their comorbidities (co-occurrences in patients). A novel embedding model is designed to extract knowledge from disease comorbidities by learning from a large-scale EHR database comprising more than 35 million inpatient cases spanning nearly a decade, revealing significant improvements on disease phenotyping over current computational approaches. In addition, the use of the proposed methodology is extended to discover novel disease-gene associations by including valuable domain knowledge from genome-wide association studies. To evaluate our approach, its effectiveness is compared against a held-out set where, again, it revealed very compelling results. For selected diseases, we further identify candidate gene lists for which disease-gene associations were not studied previously. Thus, our approach provides biomedical researchers with new tools to filter genes of interest, thus, reducing costly lab studies.Citation to related work
Springer Science and Business Media LLCHas part
Scientific ReportsADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.eduae974a485f413a2113503eed53cd6c53
http://dx.doi.org/10.34944/dspace/5010
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