首页|Study Results from University of Missouri Provide New Insights into Machine Lear ning (Maivess: Streamlined Selection of Antigenically Matched, High-yield Viruse s for Seasonal Influenza Vaccine Production)
Study Results from University of Missouri Provide New Insights into Machine Lear ning (Maivess: Streamlined Selection of Antigenically Matched, High-yield Viruse s for Seasonal Influenza Vaccine Production)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news originating from Columbia, Missouri, by NewsR x correspondents, research stated, "Vaccines are the main pharmaceutical interve ntion used against the global public health threat posed by influenza viruses. T imely selection of optimal seed viruses with matched antigenicity between vaccin e antigen and circulating viruses and with high yield underscore vaccine efficac y and supply, respectively." Financial support for this research came from U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases. Our news journalists obtained a quote from the research from the University of M issouri, "Current methods for selecting influenza seed vaccines are labor intens ive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccin e strains directly from clinical samples by using molecular signatures of antige nicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine can didates and experimentally confirm that these candidates have optimal antigenici ty and growth in cells and eggs. Our framework can potentially reduce the optima l vaccine candidate selection time from months to days and thus facilitate timel y supply of seasonal vaccines. Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses."
ColumbiaMissouriUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversi ty of Missouri
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