NEW DELHI — Artificial intelligence (AI) could soon play a pivotal role in
diagnosing and treating genetic disorders, according to a study.
The study, led by researchers at the Australian National
University (ANU) in Australia marks a significant step toward more precise,
personalised medicine by harnessing the power of new data tools, Xinhua news
agency reported.
Published in Nature Communications, the study combines
AI-powered protein models with genome sequencing to better understand how
mutations affect human health.
It uncovered why some proteins are more vulnerable to
harmful mutations than others, through the use of Google DeepMind AlphaFold's
cutting-edge AI to analyse the effects of every possible mutation across the
full range of human proteins.
"Our study reveals that evolution has built
resilience into the most essential proteins, shielding them from harmful
mutations that disrupt protein stability. Less critical proteins seem not to
have evolved this inherent ability to absorb damage," said research lead
Dan Andrews, Associate Professor at ANU.
Researchers from ANU's John Curtin School of Medical
Research and School of Computing help explain why seemingly less vital genes
often play a larger role in genetic conditions.
Andrews said that genetic mutations are like the rain
that all genes must endure -- they are constant and unavoidable.
While some genes are very essential and are rarely
observed, others “are a little less critical but are still important enough
that human diseases occur when they contain mutations.”
The research helps prioritise treatments by identifying
specific genetic pathways affected by mutations.
"It's important to identify which genetic system is
dysfunctional in a given person, which helps us potentially choose the most
effective treatment," Andrews said.
He noted that the study also applies to complex diseases
with multiple mutations as it involves scoring genetic variation for its
functional effects -- crucial for identifying potentially broken genes.
Further, the study also shows a potential for clinical
translation and the development of AI tools to help improve patient outcomes.
“Our future goals include developing automated systems to
flag effective treatment for individuals, based on their genetic and pathology
data,” Andrews said.