title: An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Information Systems, University of Haifa, 3498838, Israel. Electronic address: omrimug@gmail.com.
authors.forename: Omri
authors.initials: O
authors.lastname: Mugzach
authors.affiliation: Department of Information Systems, University of Haifa, 3498838, Israel. Electronic address: morpeleg@is.haifa.ac.il.
authors.forename: Mor
authors.initials: M
authors.lastname: Peleg
authors.affiliation: Department of Genetics, Stanford University, Stanford, CA 94305, United States.
authors.forename: Steven C
authors.initials: SC
authors.lastname: Bagley
authors.affiliation: Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60608, United States.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Guter
authors.affiliation: Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60608, United States.
authors.forename: Edwin H
authors.initials: EH
authors.lastname: Cook
authors.affiliation: Department of Genetics, Stanford University, Stanford, CA 94305, United States.
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000465:Algorithms; D000067877:Autism Spectrum Disorder / Q000175:diagnosis*; D001321:Autistic Disorder / Q000175:diagnosis; D001331:Automation; D015897:Comorbidity; D003625:Data Collection; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D010641:Phenotype; D011237:Predictive Value of Tests; D011336:Probability; D015203:Reproducibility of Results; D012307:Risk Factors; D011795:Surveys and Questionnaires
keywords: Autism; Diagnosis; Ontology; Ontology Web Language; Reasoning