Despite its early promise as a diagnostic and prognostic tool, gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Here, we introduce a molecular computation strategy for analysing the information contained in complex gene expression signatures without the need for costly instrumentation. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. We validate our technology with two applications: a classifier for early cancer diagnostics and a classifier for differentiating viral and bacterial respiratory infections based on host gene expression. Together, our results demonstrate a general and modular framework for low-cost gene expression analysis.

尽管其在临床上有望作为一种诊断和预后工具，但基因表达谱分析仍然在成本上是昂贵的，并且在临床环境中难以实施。在这里，我们介绍了一种分子计算策略，可用于分析复杂基因表达特征中包含的信息，而无需使用昂贵的仪器。我们的工作流程始于在标记的基因表达数据上训练计算分类器。然后在分子水平上实现这种计算机分类器，以实现表达分析和以前未表征样品的分类。通过RNA输入与工程化DNA探针之间的一系列分子相互作用进行分类，这些探针旨在根据重要性对每个输入进行不同称重。我们通过两个应用程序来验证我们的技术：用于早期癌症诊断的分类器，以及用于基于宿主基因表达区分病毒和细菌性呼吸道感染的分类器。总之，我们的结果证明了用于低成本基因表达分析的通用模块框架。