  Numerous tools have been recently developed to predict disease phenotypes
using single-cell RNA sequencing (RNA-seq) data. CloudPred is an end-to-end
differentiable learning algorithm coupled with a biologically informed mixture
model, originally tested on lupus data. This study extends CloudPred's
applications to breast cancer disease phenotype prediction to test its
robustness and applicability on untested and unrelated biological data. When
applying a breast cancer single-cell RNA seq dataset, CloudPred achieved an
area under the ROC curve (AUC) of 1 in predicting cancer status and performed
better than a linear and Deepset models.
