Using machine learning to hunt for the origins of new viruses

Image credit: Sumy Sadurni/AFP

When a new virus crops up in people, health authorities face an urgent question: Where did it come from?

Thousands of viruses are out there in the wild, circulating in animal hosts and only gaining attention when they infect people. Viruses can make that jump in various ways — sometimes through direct contact, sometimes via an intermediary like a mosquito or tick. But researchers don’t have great tools to quickly determine the reservoirs that house the viruses or the “vectors” by which they were transmitted.

On [November 1], researchers unveiled a new system, based on machine learning models, that identifies patterns in the genomes of viruses to offer a hypothesis.


The system, which was described in a paper published in Science, remains fairly crude for now; it can tell you that a virus likely resides in bats, for example, but not which species. And it’s not entirely accurate: For known viruses, it was able to identify the general type of vector 90.8 percent of the time and host reservoir type 71.9 percent of the time.

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[T]he prediction program arrives as scientists have embarked on ambitious efforts to catalogue viruses around the world, including an endeavor called the Global Virome Project, in which [disease ecologist Peter] Daszak’s group is participating. Researchers aim to get ahead of future viral threats before they strike.

Read full, original post: With machine learning, researchers get new clues in the hunt for the source of mysterious viruses

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