A team of researchers at the Cheriton School of Computer Science, along with their colleagues at Western University, have successfully classified 191 previously unidentified astroviruses using a new machine learning-enabled classification process.
Astroviruses are some of the most damaging and widespread viruses in the world. These viruses cause severe diarrhea, which kills more than 440,000 children under the age of five annually. In the poultry industry, astroviruses like avian flu have an 80 per cent infection rate and a 50 per cent mortality rate among livestock, leading to economic devastation, supply chain disruption, and food shortages.
Astroviruses mutate quickly and can spread easily across their more than 160 host species, putting researchers and public health officials in a constant race to classify and understand new astroviruses as they emerge. In 2023, there were 322 unidentified astroviruses with distinct genomes. This year, that number has risen to 479.
“At any given point, between two and nine per cent of humans carry one of these viruses,” said Fatemeh Alipour, PhD candidate at the Cheriton School of Computer Science and the lead computer science author of the research study. “That number can be as high as 30 per cent in some countries. “Understanding and classifying these viruses effectively is essential for developing vaccines.”
Left
to
right:
Cheriton
School
of
Computer
Science
Professor
Lila
Kari
and
PhD
candidate Fatemeh
Alipour.
(Study
collaborators
Professor
Yang
Lu
from
the
Cheriton
School
of
Computer
Science,
and Connor
Holmes and
Professor
Kathleen
A.
Hill
from
the
University
of
Western
Ontario
were
unavailable
for
the
photo.)
Professor Lila
Kari
is
an
author
of
more
than
200
peer-reviewed
articles,
and
is
regarded
as
one
of
the
world’s
experts
in
biomolecular
computation.
PhD
candidate Fatemeh
Alipour works
on
DNA
sequence
classification
using
alignment-free
methods
with
applications
to
the
study
of
virus-host
co-evolution.
Professor
Yang
Lu and
his
students
develop
interpretable
machine
learning
models
to
make
sense
of
complex
biological
data
and
discover
scientifically
interesting
and
statistically
confident
hypotheses
by
interpreting
these
models.
Read the full article on Waterloo News.
To learn more about this research, please see Fatemeh Alipour, Connor Holmes, Yang Young Lu, Kathleen A. Hill, Lila Kari, Leveraging machine learning for taxonomic classification of emerging astroviruses, Frontiers in Molecular Biosciences, Vol 10, 2024.