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dc.contributor.authorTan, Xiaoyan
dc.date.accessioned2018-06-06T04:30:14Z
dc.date.available2018-06-06T04:30:14Z
dc.date.issued2017-12
dc.identifier.othertan_xiaoyan_201712_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/tan_xiaoyan_201712_ms
dc.identifier.urihttp://hdl.handle.net/10724/38087
dc.description.abstractConsidering traditional animal-based testing approaches are expensive and time-consuming, the application of in vitro testing to predict patterns of toxicity was highlighted, ToxCast program was launched by EPA in 2007 with a large number of toxicology information collected from widely dispersed sources. Using the publicly available database, this analysis was performed to develop predictive toxicology models to discriminate between chemicals with reproductive toxicity and those without through examining the association between in vitro assays and in vivo lowest effect level. Three models were built to predict LEL concentration of chemicals based on in vitro measurements, which includes Support vector machine, logistic model, classification and regression tree, and cross validation was used to test the accuracy of models. Except for logistic model failed to give a clear result, SVM model and CART model showed a good prediction with the index of the area under a ROC curve.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectToxCast, SVM, logistic regression, CART
dc.titleAnalysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models
dc.typeThesis
dc.description.degreeMS
dc.description.departmentPublic Health
dc.description.majorBiostatistics
dc.description.advisorHanwen Huang
dc.description.committeeHanwen Huang
dc.description.committeeStephen Rathbun
dc.description.committeeKeivin Dobbin


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