Chagas cardiomyopathy prediction using statistical models.
To analyze and leverage the data that Dr.Seidman’s group has on hand to better understand progression to Chagas cardiomyopathy
CCB will work with Seidman Lab to help process, explore, and analyze the data that Dr. Seidman’s lab has collected. This will involve data cleaning as appropriate and re-examining the epitope quantification pipeline and batch effect correction steps. We will then test a suite of standard machine learning techniques (using random forests as a starting point) to develop a predictive classifier that uses the epitope data and other available covariates to distinguish between the Chagas cardiomyopathy and indeterminate patient categories