首页|University of Guelph Reports Findings in Influenza A Virus (Utilizing machine le arning and hemagglutinin sequences to identify likely hosts of influenza H3Nx vi ruses)
University of Guelph Reports Findings in Influenza A Virus (Utilizing machine le arning and hemagglutinin sequences to identify likely hosts of influenza H3Nx vi ruses)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on RNA Viruses - Influenz a A Virus is the subject of a report. According to news reporting out of Guelph, Canada, by NewsRx editors, research stated, “Influenza is a disease that repres ents both a public health and agricultural risk with pandemic potential. Among t he subtypes of influenza A virus, H3 influenza virus can infect many avian and m ammalian species and is therefore a virus of interest to human and veterinary pu blic health.” Our news journalists obtained a quote from the research from the University of G uelph, “The primary goal of this study was to train and validate classifiers for the identification of the most likely host species using the hemagglutinin gene segment of H3 viruses. A five-step process was implemented, which included trai ning four machine learning classifiers, testing the classifiers on the validatio n dataset, and further exploration of the best-performing model on three additio nal datasets. The gradient boosting machine classifier showed the highest host-c lassification accuracy with a 98.0 % (95 % CI [97.01, 98.73]) correct classification rate on an independent validation dataset. The classifications were further analyzed using the predicte d probability score which highlighted sequences of particular interest. These se quences were both correctly and incorrectly classified sequences that showed con siderable predicted probability for multiple hosts. This showed the potential of using these classifiers for rapid sequence classification and highlighting sequ ences of interest. Additionally, the classifiers were tested on a separate swine dataset composed of H3N2 sequences from 1998 to 2003 from the United States of America, and a separate canine dataset composed of canine H3N2 sequences of avia n origin. These two datasets were utilized to look at the applications of predic ted probability and host convergence over time. Lastly, the classifiers were use d on an independent dataset of environmental sequences to explore the host ident ification of environmental sequences.”
GuelphCanadaNorth and Central Americ aCyborgsEmerging TechnologiesInfluenza A VirusMachine LearningRNA Viru sesRisk and PreventionViralVirology
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