Journal
GENETICS IN MEDICINE
Volume 20, Issue 1, Pages 151-158Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/gim.2017.108
Keywords
biochemical genetics; bioinformatics; database; inherited metabolic diseases; phenomics
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Funding
- Michael Smith Foundation for Health Research
- Jan M. Friedman Studentship from BC Children's Hospital Foundation
- EU [FP7-HEALTH-2012-INNOVATION-1, 305444]
- Canadian Institutes of Health Research
- Genome Canada/Genome BC/CIHR Large Scale Applied Research Grant ABC4DE project [174CDE]
- Dietmar Hopp Foundation
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Purpose: Recognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. Focusing on inborn errors of metabolism (IEMs), we present a method that brings the knowledge of highly specialized experts to professionals involved in early diagnoses. We introduce IEMbase, an online expert-curated IEM knowledge base combined with a prototype diagnosis support (mini-expert) system. Methods: Disease-characterizing profiles of specific biochemical markers and clinical symptoms were extracted from an expert-compiled IEM database. A mini-expert system algorithm was developed using cosine similarity and semantic similarity. The system was evaluated using 190 retrospective cases with established diagnoses, collected from 15 different metabolic centers. Results: IEMbase provides 530 well-defined IEM profiles and matches a user-provided phenotypic profile to a list of candidate diagnoses/genes. The mini-expert system matched 62% of the retrospective cases to the exact diagnosis and 86% of the cases to a correct diagnosis within the top five candidates. The use of biochemical features in IEM annotations resulted in 41% more exact phenotype matches than clinical features alone. Conclusion: IEMbase offers a central IEM knowledge repository for many genetic diagnostic centers and clinical communities seeking support in the diagnosis of IEMs.
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