Abstract:Binary classification using gene expression data is commonly used to stratify cancers into molecular subgroups that may have distinct prognoses and therapeutic options. A limitation of many such methods is the requirement for comparable training and testing data sets. Here, we describe and demonstrate a self-training implementation of probability ratio-based classification prediction score (PRPS-ST) that facilitates the porting of existing classification models to other gene expression data sets. We demonstrat… Show more
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