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MIT researchers develop AI tool to improve flu vaccine strain selection
MIT's “VaxSeer” AI system predicts dominant flu strains months ahead and identifies the most protective vaccine candidates. It uses deep learning models that were trained on viral sequences and lab test results to potentially make vaccine selection more accurate.
“VaxSeer adopts a large protein language model to learn the relationship between dominance and the combinatorial effects of mutations,” explains Wenxian Shi, a PhD student in MIT’s Department of Electrical Engineering and Computer Science, researcher at CSAIL, and lead author of a new paper on the work. “By modeling how viruses evolve and how vaccines interact with them, AI tools like VaxSeer could help health officials make better, faster decisions — and stay one step ahead in the race between infection and immunity,” says Shi. “This paper is impressive, but what excites me perhaps even more is the team’s ongoing work on predicting viral evolution in low-data settings,” says Assistant Professor Jon Stokes of the Department of Biochemistry and Biomedical Sciences at McMaster University in Hamilton, Ontario.
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