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Optimizing Gene Expression Prediction And Omics Integration In Populations Of African Ancestry, Paul Chukwuebuka Okoro
Optimizing Gene Expression Prediction And Omics Integration In Populations Of African Ancestry, Paul Chukwuebuka Okoro
Master's Theses
Popular transcriptome imputation methods such as PrediXcan and FUSIon use parametric linear assumptions, and thus are unable to flexibly model the complex genetic architecture of the transcriptome. Although non-linear modeling has been shown to improve imputation performance, replicability and potential cross-population differences have not been adequately studied. Therefore, to optimize imputation performance across global populations, we used the non-linear machine learning (ML) models random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN) to build transcriptome imputation models, and evaluated their performance in comparison to elastic net (EN). We trained gene expression prediction models using genotype and blood …
Opioid Misuse Detection In Hospitalized Patients Using Convolutional Neural Networks, Brihat Sharma
Opioid Misuse Detection In Hospitalized Patients Using Convolutional Neural Networks, Brihat Sharma
Master's Theses
Opioid misuse is a major public health problem in the world. In 2016, 11.3 million people were reported to misuse opioids in the US only. Opioid-related inpatient and emergency department visits have increased by 64 percent and the rate of opioid-related visits has nearly doubled between 2009 and 2014. It is thus critical for healthcare systems to detect opioid misuse cases. Patients hospitalized for consequences of their opioid misuse present an opportunity for intervention but better screening and surveillance methods are needed to guide providers. The current screening methods with self-report questionnaire data are time-consuming and difficult to perform in …