Artificial intelligence predicts deadly virus in humans

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Most new human infectious diseases come from animals, and scientists suggest they can be detected in a timely manner to save lives with the right technology.

Recent reports have shown that artificial intelligence may be able to detect an animal-to-human virus before it becomes a pandemic.

Many scientists believe that the coronavirus, which is spreading around the world, came from bats, in a process known as zoonosis.

Early detection of high-risk viruses could improve research and surveillance priorities.

Nardos Mullintz, Simon Babian and Daniel Stryker of the University of Glasgow, who led the study, suggested that machine learning (a type of artificial intelligence) using viral genomes could predict the likelihood that given biologically relevant risk An animal virus will infect humans. .

The scientists explained that their study, published Tuesday, September 28, in the journal PLOS Biology, could be a major breakthrough, as identifying zoonotic diseases before they appear is a major challenge because only one in 1.67 million animal viruses Only a small minority is capable. to infect humans.

To develop a machine-learning model using viral genome sequencing, the scientists first compiled a data set of 861 virus species from 36 families. They then created machine learning models that determined the likelihood of a human being infected based on patterns in the virus’s genome. The team then applied the best-performing model to analyze patterns in the predicted zoonotic potential of additional virus genomes from the species group.

“Our findings add a significant portion to the already surprising information that we can extract from the genetic sequences of viruses using artificial intelligence techniques,” the scientists wrote in their paper.

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The study indicated that artificial intelligence, developed by scientists, could help identify “Covid-19” before the killings began in Wuhan, China, in late 2019.

Co-scientist Molentz told The Daily Beast, “The ability to predict whether a virus can infect humans by sequencing the genome, while still working reliably, for an entirely new virus, What the model hasn’t seen sets it apart in other ways.”

Mullings and his team at the University of Glasgow helped research scientists at the University of Liverpool earlier this year see the potential of artificial intelligence in the field of human-animal viruses.

According to SciTechDaily, the scientists say: “Our results suggest that the potential of zoonotic viruses can be inferred to an astonishingly large extent from their genome sequences.”

“By uncovering the viruses with the greatest potential to become zoonoses, genome-based classification allows more environmental and viral characteristics to be targeted more effectively,” the team continued.

The team continued: “The genetic sequence is usually the first, and often the only, information we have about the newly discovered virus, and the more information we can extract from it, the faster we can identify the origin and origin of the virus.” Can determine risks to animals. It can pose.”

“As more viruses are characterized, our machine learning models will become more effective in identifying rare viruses that need to be closely monitored and prioritized for preventive vaccine development,” they wrote.

While the mechanistic models developed by the team predict whether viruses are capable of infecting humans, the ability to infect is only part of the risk of spreading zoonotic diseases, the ability of the virus to transmit between humans. and is also affected by the environment. Conditions in humans The time a person has come into contact with.

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