AI may predict the next virus to jump from animals to humans

Date:
September 28, 2021
Source:
PLOS
Summary:
A new study suggests that machine learning using viral genomes
may predict the likelihood that any animal-infecting virus will
infect humans, given biologically relevant exposure.



FULL STORY ==========================================================================
Most emerging infectious diseases of humans (like COVID-19) are zoonotic -
- caused by viruses originating from other animal species. Identifying
high- risk viruses earlier can improve research and surveillance
priorities. A study publishing in PLOS Biology on September 28th by
Nardus Mollentze, Simon Babayan, and Daniel Streicker at University
of Glasgow, United Kingdom suggests that machine learning (a type of
artifical intelligence) using viral genomes may predict the likelihood
that any animal-infecting virus will infect humans, given biologically
relevant exposure.


========================================================================== Identifying zoonotic diseases prior to emergence is a major challenge
because only a small minority of the estimated 1.67 million animal
viruses are able to infect humans. To develop machine learning models
using viral genome sequences, the researchers first compiled a dataset
of 861 virus species from 36 families.

They then built machine learning models, which assigned a probability
of human infection based on patterns in virus genomes. The authors then
applied the best-performing model to analyze patterns in the predicted
zoonotic potential of additional virus genomes sampled from a range
of species.

The researchers found that viral genomes may have generalizable
features that are independent of virus taxonomic relationships and may
preadapt viruses to infect humans. They were able to develop machine
learning models capable of identifying candidate zoonoses using viral
genomes. These models have limitations, as computer models are only a preliminary step of identifying zoonotic viruses with potential to infect humans. Viruses flagged by the models will require confirmatory laboratory testing before pursuing major additional research investments. Further,
while these models predict whether viruses might be able to infect
humans, the ability to infect is just one part of broader zoonotic risk,
which is also influenced by the virus' virulence in humans, ability to
transmit between humans, and the ecological conditions at the time of
human exposure.

According to the authors, "Our findings show that the zoonotic potential
of viruses can be inferred to a surprisingly large extent from their
genome sequence. By highlighting viruses with the greatest potential
to become zoonotic, genome-based ranking allows further ecological
and virological characterisation to be targeted more effectively."
"These findings add a crucial piece to the already surprising amount
of information that we can extract from the genetic sequence of viruses
using AI techniques," Babayan adds. "A genomic sequence is typically the
first, and often only, information we have on newly-discovered viruses,
and the more information we can extract from it, the sooner we might
identify the virus' origins and the zoonotic risk it may pose. As more
viruses are characterized, the more effective our machine learning
models will become at identifying the rare viruses that ought to be
closely monitored and prioritized for preemptive vaccine development." ========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Nardus Mollentze, Simon A. Babayan, Daniel G. Streicker. Identifying
and
prioritizing potential human-infecting viruses from their genome
sequences. PLOS Biology, 2021; 19 (9): e3001390 DOI: 10.1371/
journal.pbio.3001390 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210928141839.htm

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