Predicting probability distributions for cancer therapy drug selection optimization
This addresses the challenge of high variability in cell lines for cancer therapy, though it appears incremental by proposing basic tools for distribution prediction.
The paper tackles the problem of drug selection for cancer therapy by predicting entire probability distributions instead of single values, showing superiority in optimizing selection for extreme statistics.
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.