Health
Machine learning can predict which animal viruses, such as Covid-19, are at risk of infecting humans
NS Machine learning Scientists have developed a way to accurately predict which animal viruses, such as SARS and Covid-19, could infect humans in the future.
It interprets the information encoded in the viral genome. This is often the only scientifically known information about new animal viruses.
The new method can also rank viruses as low, medium, high, or very high risk.
Without prior knowledge of previous SARS outbreaks in humans, this model shows SARS-CoV-2, the virus that caused the Covid-19 pandemic, and its closest relatives found in animals. I was able to accurately predict that I had a high risk. Can infect humans. This finding, along with more formal testing of hundreds of viruses with known zoonotic conditions, showed that models can make accurate predictions about viruses that are entirely new to science.
Most emerging human infectious diseases are caused by zoonotic viruses from other animal species. However, of the millions of viruses that circulate in animals, only a few can infect humans.
Scientists are currently very limited in their ability to quickly assess the risk of zoonotic diseases when they are discovered, so which newly discovered virus should be prioritized for early investigation? Is difficult to know.
The researchers at the University of Glasgow behind the study are an important step towards preparing and planning for future human development.
Senior author Daniel Striker said:
“Our new genome-based zoonotic risk assessment represents a step towards solving that challenge, a previous effort to show that viral reservoir hosts and arthropod vectors can be predicted from the viral genome. Together, it shows that a surprising amount of ecological insights are possible, suggesting the existence of poorly understood methods for the virus to adapt to the host from the genomic sequence alone.
“Soon, these models use only sequence sequences, so they can be applied when a virus is discovered, and a fast, low-cost triage system to determine which virus is worth special attention. Create a.”
The study is published at PLOS Biology..
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