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Researchers at Google DeepMind claim to have built an AI model that can pinpoint which genetic mutations are likely to cause disease, according to a new study in the journal Science.
The new model, dubbed AlphaMissense, is an adaptation of AlphaFold, the DeepMind breakthrough that, back in 2020, finally cracked the protein folding problem, which had baffled the scientific community for years. According to the new study, AlphaMissense is “fine-tuned” on “human and primate” genetic variance and specifically trained to identify “missense” mutations, or genetic mutations that take place in a single letter of DNA code.
Though some missense mutations are completely benign — any given human has 9,000 or so missense alleles in their DNA — others can cause serious disease; sickle cell anemia, cystic fibrosis, and cancer, as DeepMind noted in a Tuesday blog post, all stem from missense genes specifically. And yet, despite the fact that missense mutations and other DNA abnormalities are a primary driver of illness, humans have only been able to independently classify a minuscule 0.1 percent of missense genes as good or bad.
Until now, that is. According to DeepMind’s new study, this new AI model has been able to identify a staggering 71 million missense mutations, and from there has been able to predictively classify 89 percent of these variations as “either likely benign or likely pathogenic.” Tens of millions of these predictions have since been spun into a vast online database for physicians, genetic researchers, and other diagnostic experts, who according to Google will hopefully be able to use this new resource to find and diagnose various illnesses — including exceedingly rare disorders — and ultimately kickstart the development of what it called “life-saving treatments.”
“Today, we’re releasing a catalog of ‘missense’ mutations where researchers can learn more about what effect they may have,” DeepMind penned in its Tuesday blog, adding later that “by using AI predictions, researchers can get a preview of results for thousands of proteins at a time, which can help to prioritize resources and accelerate more complex studies.”
But while that all sounds great, the news has been met with mixed reactions from the scientific community.
Some folks, like Ewan Birney, deputy director general of the European Molecular Biology Laboratory, told the BBC that AlphaMissense is a “big step forward,” arguing that the model “will help clinical researchers prioritize where to look to find areas that could cause disease.” But others, like Ben Lehner, a senior group leader in human genetics at the UK’s Wellcome Sanger Institute, were more hesitant, telling The Guardian that the black-box aspect of the tech concerns him.
“One concern about the DeepMind model is that it is extremely complicated,” Lehner told The Guardian. “A model like this may turn out to be more complicated than the biology it is trying to predict,” he added, noting that because doctors don’t really understand how models like AlphaMissense actually work, using their predictions to make diagnostic choices might prove problematic.
“It’s humbling to realize that we may never be able to understand how these models actually work. Is this a problem?” Lehner told the Guardian. “It may not be for some applications, but will doctors be comfortable making decisions about patients that they don’t understand and can’t explain?”
That said, though, Lehner did note that the DeepMind model “does a good job of predicting what is broken,” and that “knowing what is broken is a good first step.” Still, he says, you “also need to know how something is broken if you want to fix it.”
AlphaMissense, of course, doesn’t quite go that far just yet. After all, genetics is endlessly complicated. As Heidi Rehm, who directs the clinical laboratory at the Broad Institute of MIT and Harvard, told The MIT Technology Review, computer predictions are only “one piece of evidence” from which physicians can make diagnostic calls.
“The models are improving, but none are perfect, and they still don’t get you to pathogenic or not,” Rehm continued, reportedly noting that she was “disappointed” to see Google exaggerate the medical efficacy of its new product.
So, mixed reviews. But even if DeepMind’s purported step forward isn’t quite as big as the venture has cracked it up to be, it may well be a step forward nonetheless. Only time will tell — but in the meantime, if you’re in the business of diagnosing genetic disorders, maybe just take AlphaMissense’s predictions with a grain of salt.
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