As global demand for digital storage capacity grows, storage technologies based on synthetic DNA have emerged as a dense and durable alternative to traditional media. Existing approaches leverage robust error correcting codes and precise molecular mechanisms to reliably retrieve speci\ufb01c \ufb01les from large databases. Typically, \ufb01les are retrieved using a prespeci\ufb01ed key, analogous to a \ufb01lename. However, these approaches lack the ability to perform more complex computations over the stored data, such as similarity search: e.g., \ufb01nding images that look similar to an image of interest without prior knowledge of their \ufb01le names. Here we demonstrate a technique for executing similarity search over a DNA-based database of 1.6 million images. Queries are implemented as hybridization probes, and a key step in our approach was to learn an image-to-sequence encoding ensuring that queries preferentially bind to targets representing visually similar images. Experimental results show that our molecular implementation performs comparably to state-of-the-art in silico algorithms for similarity search.<\/p>\n","protected":false},"excerpt":{"rendered":"
As global demand for digital storage capacity grows, storage technologies based on synthetic DNA have emerged as a dense and durable alternative to traditional media. Existing approaches leverage robust error correcting codes and precise molecular mechanisms to reliably retrieve speci\ufb01c \ufb01les from large databases. Typically, \ufb01les are retrieved using a prespeci\ufb01ed key, analogous to a 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