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Noise-precision tradeoff in predicting combinations of mutations and drugs

机译:预测突变和药物组合时的噪声精确权衡

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摘要

Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.
机译:许多生物学问题涉及对多种扰动的反应。例子包括对多种药物组合的反应,以及多种突变组合的影响。这些问题的组合空间呈指数增长,这使得通过实验覆盖整个空间变得不可行。为了克服这个问题,已经提出了几种预测药物组合或适应度值的公式。这些公式使用单个摄动和成对摄动的影响来预测三重态和更高阶的组合。有趣的是,不同的公式在不同的数据集上表现最佳。在这里,我们使用帕累托最优理论定量地解释了为什么由于固有的偏差-方差(噪声精度)的折衷,没有公式对于所有数据集都是最优的。我们计算了对数线性公式的Pareto前沿,发现最佳公式取决于数据集的属性:典型的相互作用强度和实验噪声。这项研究提供了一种为给定的数据集选择合适的预测公式的方法,以最好地克服组合爆炸问题。

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