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

Date: Sept. 2, 2016

Speaker: Mor Peleg, University of Haifa, Israel

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

In my talk I'd like to discuss an ontology that my former Masters student, Omri Mugzach and I developed in OWL and SWRL using Protege. Our goal was to create an ontology that will allow data integration and reasoning with subject data to infer new knowledge on autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDD). We took a first step toward this goal by extending an existing autism ontology that was developed by Samson as part of Amar Das' autism project, which allowed 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. We then added knowledge regarding diagnostic instruments, ASD phenotypes and risk factors to augment Tu’s ontology via OWL class definitions and semantic web rules. Overall 443 classes and 632 rules were added, which represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms related to DSM definitions. 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. 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.

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.

References

• Omri Mugzach, Mor Peleg, Steven C. Bagley, Stephen J. Guter, Edwin H. Cook, Russ B. Altman. “An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview–Revised data”. Journal of BioMedical Informatics 2015;56:333-47.
• Tu SW, Tennakoon L, Connor MO, Das A. Using an Integrated Ontology and Information Model for Querying and Reasoning about Phenotypes : The Case of Autism. AMIA Annu Symp Proc. 2008. p. 727–31.
• McCray AT, Trevvett P, Frost HR. Modeling the Autism Spectrum Disorder Phenotype. Neuroinformatics. 2013 Oct;12(2):291–305