• Otávio Santiago

AI Tool Eve Accurately Predicts Disease Relevance of Human Genetic Variants


Understanding how the wealth of genetic variation in the human genome impacts on disease could potentially transform healthcare, but while we know the consequences of perhaps a handful of specific genetic mutations, our ability to interpret the meaning of millions of genetic variations identified through genome sequencing remains a challenge.


Researchers at Harvard Medical School and Oxford University have now developed an artificial intelligence (AI) tool called EVE (evolutionary model of variant effect), which uses a sophisticated type of machine learning to detect patterns of genetic variation across hundreds of thousands of nonhuman species and then use them to make predictions about the meaning of variations in human genes.

In a study published in Nature, the team used EVE to assess 36 million protein sequences and 3,219 disease-associated genes across multiple species. Their results suggested that 256,000 previously identified human gene variants currently of unknown significance should, in fact, be reclassified as either benign or disease causing. While the researchers emphasize that EVE is not a diagnostic test, they say it could augment current clinical tools used by geneticists and other physicians to make diagnoses, predict disease progression, and even choose treatment based on the presence of certain disease-causing genetic mutations. “Increasingly, people have access to sequencing their genomes, but making sense of the data is not always straightforward,” said study senior author Debora Marks, PhD, associate professor of systems biology in the Blavatnik Institute at HMS.


“There is very little information about what it even means for likelihood of disease or disease progression … We believe our approach can be used as an added tool in current clinical assessments and offers a powerful new way to reduce uncertainty and clarify decision-making, particularly in the clinical setting.


Please, to access the full article visit GEN

biotechdesign.io