Sample, financial and labor requirements are key barriers to scaling up high-content phenotypic discovery efforts. A broadly applicable method overcomes these challenges through experimental compression (by pooling various perturbations) and computational deconvolution (of their individual effects), empowering the use of phenotypic screening to advance therapeutic discovery.

中文翻译：

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通过压缩表型筛选扩展发现工作的框架


样本、财务和劳动力要求是扩大高内涵表型发现工作的主要障碍。一种广泛适用的方法通过实验压缩（通过汇集各种扰动）和计算反卷积（它们的各个效应）克服了这些挑战，从而能够使用表型筛选来推进治疗发现。