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Genet Med. 2018 Jan;20(1):151-158. doi: 10.1038/gim.2017.108. Epub 2017 Jul 20.

Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism.

Author information

1
Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
2
Genome Science and Technology Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada.
3
Division of Metabolism, University Children's Hospital, Zurich, Switzerland.
4
Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.
5
Dietmar-Hopp Metabolic Center, Department of General Pediatrics, University Hospital, Heidelberg, Germany.
6
Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.
7
Departments of Pediatrics and Clinical Genetics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.

Abstract

PurposeRecognizing 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.MethodsDisease-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.ResultsIEMbase 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.ConclusionIEMbase 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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