A transformer-based model identified 18 genetic variants linked to ischaemic stroke from DNA
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
Algorithms are learning to read whole DNA and may help identify people at high risk in advance.
Researchers at the University of Technology Sydney trained a transformer model on data from more than 40,000 people in two medical datasets. According to the study, participants in the high-risk group were 21% more likely to suffer a first ischaemic stroke than the low-risk group. The genes found include EDEM2 (active in the brain cortex) and KANK2 (in blood). The authors point out that classical polygenic scores do not account for interactions between variants or their position; genetics explains about 38% of the risk, so the next step is to combine genetics with clinical data and lifestyle.
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