New AI Model Enhances Detection of Rare Genetic Diseases
An international research team has introduced an advanced artificial intelligence model designed to predict the severity of previously unidentified human genetic mutations. This development could significantly improve the diagnosis and treatment of rare diseases that impact hundreds of millions of people worldwide.
The model, named "popEVE," is the result of collaboration between scientists at the Genome Organization Center in Barcelona and Harvard Medical School. It builds on a previous algorithm from 2021 called EVE, significantly enhancing its capabilities. The findings were published in the esteemed journal "Nature Genetics."
popEVE utilizes evolutionary data from hundreds of thousands of animal species, achieving high accuracy that outperforms competing models, including Google's AlphaMissense. In tests, it identified the most harmful mutation with 98% accuracy among 513 new genetic mutations and uncovered 123 genes previously unlinked to developmental disorders.
The model excels in predicting disease severity in individuals of non-European descent, addressing a critical gap in genetic diagnostics.
popEVE aims to assist healthcare providers in navigating complex medical cases, particularly those involving subtle genetic factors. It has already shown effectiveness in Senegal, where it aided in treating a muscular dystrophy patient with an increased dose of vitamin B2.
One of the model's key advantages is its low operational energy requirements, making it suitable for use in low- and middle-income countries, thereby enhancing access to advanced diagnostic tools.
Professor Damian Smedley, a computational genomics expert at Queen Mary University of London, emphasized that this model significantly expands the original's capabilities, stating: "The ability to systematically evaluate all variants in a patient's genome is essential for realizing the full potential of genome sequencing in healthcare."
Jonathan Fraser, a researcher at the Genome Organization Center, highlighted the challenge the model addresses, noting that "the vast number of unexplained genetic mutations complicates diagnosis," and added, "We hope to provide a comprehensive tool to aid in the diagnostic and treatment processes."
This scientific advancement is poised to open new avenues in genetic medicine, offering healthcare professionals and researchers enhanced tools to understand rare diseases and develop tailored treatment strategies based on detailed genetic analyses.
