A molecular multi-gene classifier for disease diagnostics
A molecular multi-gene classifier for disease diagnostics
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DOI：
10.1038/s41557-018-0056-1
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发表时间：
2018-07-01
期刊：
影响因子：
21.8
通讯作者：
Seelig, Georg
中科院分区：
文献类型：
--
作者：
Lopez, Randolph;Wang, Ruofan;Seelig, Georg
尽管基因表达谱作为诊断和预后工具的早期承诺，但在临床环境中实施仍然成本高昂且具有挑战性。在这里，我们介绍了一种分子计算策略，用于分析复杂的基因表达签名中包含的信息，而不需要昂贵的仪器。我们的工作流程开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，以实现先前未表征的样品的表达分析和分类。分类是通过RNA输入和工程DNA探针之间的一系列分子相互作用发生的，这些探针被设计成根据其重要性对每个输入进行不同的加权。我们通过两个应用程序验证了我们的技术：用于早期癌症诊断的分类器和基于宿主基因表达区分病毒和细菌呼吸道感染的分类器。总之，我们的研究结果证明了低成本基因表达分析的一般和模块化的框架。
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.