Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus

Pavan K. Attaluri, Zhengxin Chen, Aruna M. Weerakoon, Guoqing Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at

Original languageEnglish (US)
Title of host publicationCutting-Edge Research Topics on Multiple Criteria Decision Making
Subtitle of host publication20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings
Number of pages7
StatePublished - 2009

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


  • Bioinformatics
  • Decision tree
  • Hidden Markov Model
  • Influenza virus
  • Subtype prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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