Google DeepMind's new AI tool can predict genetic diseases

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Google DeepMind's new AI tool can predict genetic diseases
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Google DeepMind has come up with an AI model which can predict which genetic mutation is benign and which is hostile to the human body.

It is also releasing a catalog of 71 million possible variants that can affect the function of human proteins. In some cases, they can lead to diseases such as cystic fibrosis, sickle-cell anemia, or cancer, said Google DeepMind’sDeepMind believes that its new AI model has the potential to improve the diagnosis of rare genetic disorders and help discover new disease-causing genes. Doctors can better “understand diseases and develop new life-saving treatments.

“AI tools that can accurately predict the effect of variants have the power to accelerate research across fields from molecular biology to clinical and statistical genetics,” said the researchers in the press release.

“The goal here is, you give me a change to a protein, and instead of predicting the protein shape, I tell you: Is this bad for the human that has it?” says Stephen Hsu, a physicist at Michigan State University who works on genetic problems with AI techniques. “Most of these flips, we have no idea whether they cause sickness.”

Predicting the effects of a particular variant is an expensive and labor-intensive process. By leveraging AI tools, research in this arena can be accelerated. “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,” said the researchers.AlphaMissense is based on DeepMind’s other model, AlphaFold, which can predict 3D models of protein structures.

The team thinks these predictions made by its latest model can help “solve open questions at the heart of genomics and across biological science.”The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity.

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