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BAYESIAN ANALYTICAL APPROACHES FOR METABOLOMICS: A NOVEL METHOD FOR MOLECULAR STRUCTURE-INFORMED METABOLITE INTERACTION MODELING, A NOVEL DIAGNOSTIC MODEL FOR DIFFERENTIATING MYOCARDIAL INFARCTION TYPE, AND APPROACHES FOR COMPOUND IDENTIFICATION GIVEN MASS SPECTROMETRY DATA Patrick J. Trainor July 19, 2018 Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, which we define as the testing of hypotheses that are more complex than single metabolite hypothesis tests. This methodology utilizes informative priors that are generated via the analysis of molecular structure similarity to enable the estimation of metabolite “interactomes” (or probabilistic models) which are organism-, sample media-, and condition-specific as well as comprehensive; and that can serve as reference models for studying perturbations in metabolic

Bayesian analytical approaches for metabolomics : a novel method for molecular structure-informed metabolite interaction modeling, a novel diagnostic model for differentiating myocardial infarction type, and approaches for compound identification given mass spectrometry data.