A new AI tool developed by Harvard Medical School researchers could significantly improve how we diagnose and understand rare genetic diseases.
An example output from the popEVE portal. The left and center panels show variant scores in chart and list formats, ranging from most likely to cause disease (dark purple) to least likely (yellow). The right panel depicts a protein crystal structure colored with variant scores. Image Credit: Marks Lab, Harvard Medical School
Every human genome contains tens of thousands of small genetic changes, known as variants, that affect how cells make proteins. Yet only a few of these actually cause disease. The challenge for scientists has been identifying the harmful variants hidden among the vast majority that are harmless.
To address this, a team from Harvard Medical School (HMS) and collaborators has introduced popEVE, a machine learning model that scores each variant in a person’s genome based on its likelihood of causing disease. Unlike many previous tools, popEVE places these variants on a continuous spectrum, making it easier to prioritize them for diagnosis and research.
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