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Full-Text Articles in Life Sciences
Leveraging Summary Statistics To Make Inferences About Complex Phenotypes In Large Biobanks, Angela Gasdaska, Derek Friend, Rachel Chen, Jason Westra, Matthew Zawistowski, William Lindsey, Nathan L. Tintle
Leveraging Summary Statistics To Make Inferences About Complex Phenotypes In Large Biobanks, Angela Gasdaska, Derek Friend, Rachel Chen, Jason Westra, Matthew Zawistowski, William Lindsey, Nathan L. Tintle
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As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In …
Implementing And Evaluating A Gaussian Mixture Framework For Identifying Gene Function From Tnseq Data, Kevin Li, Rachel Chen, William Lindsey, Aaron Best, Matthew Dejongh, Christopher Henry, Nathan L. Tintle
Implementing And Evaluating A Gaussian Mixture Framework For Identifying Gene Function From Tnseq Data, Kevin Li, Rachel Chen, William Lindsey, Aaron Best, Matthew Dejongh, Christopher Henry, Nathan L. Tintle
Faculty Work Comprehensive List
The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell's fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization …