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# High efficiency classification of children with autism spectrum disorder.

## Author(s): Li, Genyuan; Lee, Olivia; Rabitz, Herschel

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 Abstract: Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD is diagnosed mainly with psychometric tools, often unable to provide an early and reliable diagnosis. Recently, biochemical methods are being explored as a means to meet the latter need. For example, an increased predisposition to ASD has been associated with abnormalities of metabolites in folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS). Multiple metabolites in the FOCM/TS pathways have been measured, and statistical analysis tools employed to identify certain metabolites that are closely related to ASD. The prime difficulty in such biochemical studies comes from (i) inefficient determination of which metabolites are most important and (ii) understanding how these metabolites are collectively related to ASD. This paper presents a new method based on scores produced in Support Vector Machine (SVM) modeling combined with High Dimensional Model Representation (HDMR) sensitivity analysis. The new method effectively and efficiently identifies the key causative metabolites in FOCM/TS pathways, ranks their importance, and discovers their independent and correlative action patterns upon ASD. Such information is valuable not only for providing a foundation for a pathological interpretation but also for potentially providing an early, reliable diagnosis ideally leading to a subsequent comprehensive treatment of ASD. With only tens of SVM model runs, the new method can identify the combinations of the most important metabolites in the FOCM/TS pathways that lead to ASD. Previous efforts to find these metabolites required hundreds of thousands of model runs with the same data. Publication Date: 15-Feb-2018 Citation: Li, Genyuan, Lee, Olivia, Rabitz, Herschel. (2018). High efficiency classification of children with autism spectrum disorder.. PloS one, 13 (2), e0192867 - ?. doi:10.1371/journal.pone.0192867 DOI: doi:10.1371/journal.pone.0192867 ISSN: 1932-6203 EISSN: 1932-6203 Pages: 13.2:e0192867-1 - e0192867-23 Language: eng Type of Material: Journal Article Journal/Proceeding Title: PloS one Version: Final published version. This is an open access article. Notes: PLoS ONE. Volume 13, Issue 2, February 2018, Article number e0192867.

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