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|Title:||Identification of potential critical virulent sites based on hemagglutinin of influenza A virus in past pandemic strains||Authors:||Yin, Rui
Fransiskus, Xaverius Ivan
Chow, Vincent T. K.
Kwoh, Chee Keong
|Keywords:||Engineering::Computer science and engineering
Influenza A Virus
|Issue Date:||2017||Source:||Yin, R., Zhou, X., Fransiskus, X. I., Zheng, J., Chow, V. T. K., & Kwoh, C. K. (2017). Identification of potential critical virulent sites based on hemagglutinin of influenza A virus in past pandemic strains. Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science - ICBBS '17. doi:10.1145/3121138.3121166||Abstract:||The influenza pandemics have caused millions of deaths and enormous economic loss. Current circulating influenza viruses in human, avian, swine and other animals are potential to evolve into novel strains that may cause another pandemic in the future. Hence, recognizing the determinants of pandemic strains helps to raise the alarm of future pandemics. With increasingly huge biological data, computational modeling is a good technique for analyzing data, providing novel insight into significant patterns and rules. Here we define a binary classification problem of categorizing influenza strains into pandemic and non-pandemic classes based on amino acid sequences. Three rule-based algorithms are applied, namely OneR, JRip and PART, to extract rules, composed of potential critical virulent sites. The results present good performance in term of accuracy, specificity, sensitivity and F-measure (more than 0.9 on average for each). Fourteen out of the sixteen potential critical virulent sites detected in our experiments are overlapped with receptor binding sites or antigenic sites. In addition, some variations occurred in these sites are known to affect the pathogenicity of influenza viruses or to cause more severe symptom in the infected patients. The pandemic potential of uncovered sites in our study needs to be further experimentally validated.||URI:||https://hdl.handle.net/10356/102511
|DOI:||https://doi.org/10.1145/3121138.3121166||Rights:||© 2017 Association for Computing Machinery (ACM). All rights reserved. This paper was published in Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science and is made available with permission of Association for Computing Machinery (ACM).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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