About IEMbase


Inborn errors of metabolism (IEMs) represent a large class of rare genetic disorders.

For a considerable proportion of IEM, therapy is available, which dramatically improves patient outcomes. Accurate and timely diagnosis is therefore essential. However, the accuracy and timeliness of an IEM diagnosis is often difficult to achieve due to a staggering number of these rare genetic disorders, the heterogeneity of symptoms and phenotypes, as well as the extensive list of required tests and skills to properly interpret these in the context of the patient’s phenotype. By combining comprehensive expert resources on IEMs and existing ontologies - hierarchies of concepts organized as a standardized vocabulary (e.g. Human Phenotype Ontology) – we created an extensive system that aims to provide both an online knowledgebase and a smart system (artificial intelligence) for curation and diagnosis support.

Disease-characterizing profiles of clinical/biochemical (n=2323) and disorder/phenotype (n=8465) associations from an expert-generated database of 530 IEM disorders were used. These profiles were mapped to the Human Phenotype Ontology (HPO) and Logical Observation Identifiers Names and Codes (LOINC) in order to exploit the semantic relationships of symptoms from the profiles. This, in turn, allows the expert system to determine a tiered list of possible IEMs which match the user-provided symptoms.


The IEMBASE accepts an array of biochemical and clinical symptoms from a user and returns a ranked list of possible IEM disorders that match the input profile. In addition, the system can explain the rationale of its results, suggest possible tests that would assist in narrowing down the differential diagnosis, and provide access to its database of biochemical, molecular, and clinical information if more evidence is desired.