Can an AI Predict the Language of Viral Mutation?

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So far, the AI predictions are good for telling researchers what they already know. It remains to be seen whether its predictions can provide any new insights. (From 2021)

rather repetitive existence. They enter a cell, hijack its machinery to turn it into a viral copy machine, and those copies head on to other cells armed with instructions to do the same. So it goes, over and over again. But somewhat often, amidst this repeated copy-pasting, things get mixed up. Mutations arise in the copies. Sometimes, a mutation means an amino acid doesn’t get made and a vital protein doesn’t fold—so into the dustbin of evolutionary history that viral version goes.

One advantage of this idea is that it’s generalizable. To a machine learning model, a sequence is a sequence, whether it’s arranged in sonnets or amino acids. According to Jeremy Howard, an AI researcher at the University of San Francisco and a language model expert, applying such models to biological sequences can be fruitful. With enough data from, say, genetic sequences of viruses known to be infectious, the model will implicitly learn something about how infectious viruses are structured.

Still, it’s a start that could give virologists a better grip on where natural mutations are headed. “This is a phenomenal way of narrowing down the entire universe of potential mutant viruses,” says Benhur Lee, a microbiologist at Mount Sinai’s Icahn School of Medicine who wasn’t involved in the work. The predictions are only as good as the data that goes into it, he adds.

Source: Tech Daily Report (techdailyreport.net)

 

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