It is estimated that there are about 1.7 million types of viruses that attack different types of animals. Some of these have or may develop the ability to attack humans. This applies to a few of them, but how does one find out in advance which of them?
This matter was further investigated by British researchers at the University of Glasgow. They hope that in the future, artificial intelligence and machine learning can be used to beat viruses.
What the researchers found is that, to a surprising degree, it appears that the genetics of a virus determine whether or not it can become zoonotic — that is, moving from animals to humans.
This is good news, because genetic sequencing is often the first and only source of information when newly discovered viruses emerge. Thus, one has a better chance than before to quickly determine the origin of the virus and to assess the animal risks it may pose.
The more viruses that are tested and characterized in this way, the better the machine learning model will be.
“It could be a huge help in identifying rare viruses that should be closely monitored and prioritized for developing a preventative vaccine,” said Simon Papian, one of the researchers behind the project. phys.org.
step on the way
The research team began by collecting a data set of 861 known viruses from a total of 36 different virus families. They then built various machine learning models that determined the likelihood of infecting humans, based on the classification and relationship of known viruses that can infect humans.
Then they used the model that gave the best result, to analyze how likely it is that several viruses in animals could infect humans.
This provides a good basis for further laboratory research, but is still only a step in the right direction. The method the research team is developing, for example, says nothing about how easily the virus is transmitted between humans, or how suitably viruses are to actually causing disease.
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