Evolution and Vaccination of Influenza Virus

Ham Ching Lam, Xuan Bi, Srinand Sreevatsan, Daniel Boley

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this study, we present an application paradigm in which an unsupervised machine learning approach is applied to the high-dimensional influenza genetic sequences to investigate whether vaccine is a driving force to the evolution of influenza virus. We first used a visualization approach to visualize the evolutionary paths of vaccine-controlled and non-vaccine-controlled influenza viruses in a low-dimensional space. We then quantified the evolutionary differences between their evolutionary trajectories through the use of within- and between-scatter matrices computation to provide the statistical confidence to support the visualization results. We used the influenza surface Hemagglutinin (HA) gene for this study as the HA gene is the major target of the immune system. The visualization is achieved without using any clustering methods or prior information about the influenza sequences. Our results clearly showed that the evolutionary trajectories between vaccine-controlled and non-vaccine-controlled influenza viruses are different and vaccine as an evolution driving force cannot be completely eliminated.

Original languageEnglish (US)
Pages (from-to)787-798
Number of pages12
JournalJournal of Computational Biology
Issue number8
StatePublished - Aug 2017

Bibliographical note

Publisher Copyright:
© Copyright 2017, Mary Ann Liebert, Inc.


  • influenza
  • machine learning
  • visualization

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