论文标题
预测癌症治疗药物选择优化的概率分布
Predicting probability distributions for cancer therapy drug selection optimization
论文作者
论文摘要
细胞系之间的巨大差异带来了癌症治疗的药物选择的困难优化问题。标准方法为此目的使用价值预测，例如达到其分布的预期价值。本文显示了工作的优势，预测了整个概率分布 - 为此目的提出了基本工具。我们对批次中的最佳药物非常感兴趣 - 适当优化其对极端统计的选择需要了解整个概率分布，这对于在细胞系中的药物特性分布通常会变成二项式，例如取决于相应的基因。因此，对于基本的预测机制，有两个高斯人提出的混合物，试图根据其他信息来预测其体重。
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.
