An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data

Omri Mugzach, Mor Peleg, Steven C. Bagley, Stephen J. Guter, Edwin H. Cook, Russ B. Altman

Research output: Contribution to journalArticlepeer-review


Objective: Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data. Materials and methods: Knowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data. Results: We extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94. Discussion: The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy. Conclusion: The ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology.

Original languageEnglish
Pages (from-to)333-347
Number of pages15
JournalJournal of Biomedical Informatics
StatePublished - 1 Aug 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier Inc..


  • Autism
  • Diagnosis
  • Ontology
  • Ontology Web Language
  • Reasoning

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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