When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting.

中文翻译：

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RAINFOREST：一种随机森林方法，用于预测（失败的）临床药物试验数据中的治疗益处

当 III 期临床药物试验未能达到终点时，就会浪费大量资源。此外，即使临床试验证明了显着的益处，观察到的效果通常很小，并且可能不会超过药物的副作用。因此，临床上非常需要识别遗传标记的方法，这些标记可以识别可能从治疗中受益的患者亚组，因为这可以 (i) 挽救失败的临床试验和/或 (ii) 识别受益更多的患者亚组比整个人口还要多。当无法找到单一遗传生物标志物时，需要找到多变量特征的机器学习方法。对于单核苷酸多态性 (SNP) 谱，由于数据的高维性，这极具挑战性。这里，