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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorHahn, Juergen
dc.contributorMcGuinness, Deborah L.
dc.contributorIntes, Xavier
dc.contributorWan, Leo Q.
dc.contributor.authorVargason, Troy
dc.date.accessioned2021-11-03T09:13:51Z
dc.date.available2021-11-03T09:13:51Z
dc.date.created2020-06-12T12:31:04Z
dc.date.issued2019-08
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2458
dc.descriptionAugust 2019
dc.descriptionSchool of Engineering
dc.description.abstractGiven the inherent complexity of ASD, reaching this goal will necessitate a deviation from traditional medical approaches, which typically focus on single physiological mechanisms individually, towards systems-oriented and big data methodologies to investigate these systems concurrently. This work will begin with the development of a mathematical model for capturing folate-dependent one-carbon metabolism and transsulfuration activity in individuals with ASD. Estimation of personalized model parameters using clinical case-control data and calculation of parameter distributions will reveal metabolic reactions where abnormalities may be present in ASD. The next area of investigation will use multivariate statistical methods to evaluate the efficacy of biochemical measurements as potential markers for diagnosing ASD and evaluating outcomes of clinical treatment. Results will be assessed in the context of the models’ abilities to predict new data that were not used during model development. Finally, a retrospective analysis of administrative medical claims data will be performed to examine patterns in the diagnosis of co-occurring conditions in children with ASD. Studying co-occurring conditions and their contributing factors offers potential for understanding the underlying mechanisms of subgroups of ASD, which can be explored in future work.
dc.description.abstractAutism spectrum disorder (ASD) is estimated to affect 1 in 59 children in the United States. With the etiology of ASD still under general debate, the current standards for diagnosis are behavioral evaluations based on clinical observation or parent reporting. However, these evaluations are inherently subjective and do not offer an unbiased assessment of ASD status that a biomarker can offer. A common consequence of this gap in knowledge is an ASD diagnosis that is delayed by several years, which also delays behavioral interventions that could contribute to improved outcomes for individuals with the disorder. Achieving greater diagnostic accuracy along with improved outcomes for individuals with ASD will therefore require a more complete understanding of the metabolic and environmental factors contributing to the pathophysiology of the disorder.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectBiomedical engineering
dc.titleCombining systems biology and big data analytics to uncover metabolic and environmental factors in autism spectrum disorder
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179843
dc.digitool.pid179844
dc.digitool.pid179845
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Biomedical Engineering


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