Browsing by Author "Lipman, Danika"

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    A Bayesian Variable Selection Model for Semi-Continuous Response Using Gaussian Process
    (2023-09-06) Lipman, Danika; Chekouo, Thierry; Deardon, Rob; Wu, Jingjing; Lu, Xuewen; Safo, Sandra; Chekouo, Thierry; Deardon, Rob
    To my knowledge, there is not a statistical method that can perform Bayesian variable selection in a setting where there is a semi-continuous response with a non-linear relationship to predictor variables. I have developed a two-part model to accommodate a semi-continuous response, that uses Gaussian processes to capture the non-linear relationship between input variables and outcomes. Bayesian variable selection is induced in both parts of the model through the construction of the kernel matrices. I have employed the Nystr\"{o}m approximation for kernel matrices to reduce the computational complexity that occurs when working with kernel matrices and large sample sizes. I perform simulation studies and determine my method is competitive in prediction and variable selection with methods such as elastic net, and other methods that capture non-linearity such as random forests, and gradient boosted trees. In addition, I apply my method to a coronary artery disease (CAD) dataset from the Duke Database for Cardiovascular Disease (DDCD) to determine key gene expression features associated with the CAD index, a measure of CAD severity.
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    Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status
    (2023-06-12) Lipman, Danika; Safo, Sandra E.; Chekouo, Thierry
    Abstract Background There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease. Results We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease. Conclusions The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.