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.
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The authors thank Y.-J. Chen, S. Chen, G. Chatterjee and D.Y. Zhang for their support and discussions. This work was supported by NSF grants CCF-171449 and CCF-1317653.
The authors declare no competing interests.
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Lopez, R., Wang, R. & Seelig, G. A molecular multi-gene classifier for disease diagnostics. Nature Chem 10, 746–754 (2018). https://doi.org/10.1038/s41557-018-0056-1
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