  Large variability between cell lines brings a difficult optimization problem
of drug selection for cancer therapy. Standard approaches use prediction of
value for this purpose, corresponding e.g. to expected value of their
distribution. This article shows superiority of working on, predicting the
entire probability distributions - proposing basic tools for this purpose. We
are mostly interested in the best drug in their batch to be tested - proper
optimization of their selection for extreme statistics requires knowledge of
the entire probability distributions, which for distributions of drug
properties among cell lines often turn out binomial, e.g. depending on
corresponding gene. Hence for basic prediction mechanism there is proposed
mixture of two Gaussians, trying to predict its weight based on additional
information.
