According to a paper by researchers at the University of Glasgow, machine learning may be able to predict which of the 1.67 million viruses afflicting animals may cause harm to people next.
Artificial intelligence may detect the next animal-to-human virus before it spreads to the rest of the world. Most novel human infectious illnesses, like Covid-19, originate in animals. And experts believe that they might be discovered in time to save lives with the appropriate technology. The University of Glasgow’s Nardus Mollentze, Simon Babayan, and Daniel Streicker believe that a machine might predict which viruses in animals will infect people with the proper genetic material.
Only a tiny percentage of the estimated 1.67 million animal viruses that potentially harm humans can be found in their study, which was published in PLOS Biology on Tuesday.
These discoveries add a critical component to the already astonishing amount of information we can extract from the genomic sequence of viruses using AI methods,” the scientists stated. According to the paper, the researchers’ AI might have assisted in identifying Covid-19 before it killed a single person in Wuhan, China, at the end of 2019.
“The ability to forecast whether a virus can infect people from only a genome sequence, while still functioning consistently for entirely novel viruses not encountered by the model, sets it apart from prior techniques,” Mollentze, a participating scientist, told The Daily Beast.
And also, researchers at the University of Liverpool looked into AI’s potential in the field of animal-human viruses earlier this year, which aided him and his Glasgow colleagues.
“Our findings indicate that the zoonotic potential of viruses may be predicted to a very significant amount from their genomic sequence,” according to SciTechDaily.”Genome-based ranking enables more effective ecological and virological characterization by emphasizing viruses with the highest potential to become zoonotic.”
“On newly found viruses, a genomic sequence is often the first, and often only, the information we have, and the more information we can extract from it, the sooner we can determine the virus’s origins and the zoonotic danger it may represent.”
“As more viruses are characterized, our machine learning models will become more successful at identifying uncommon viruses that should be actively studied and prioritized for development of preventative vaccines.”
Please let us know your opinions in the comments section below.