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Stochastic Modeling of Temporal Variability of HIV-1 Population

  • Ilia Kiryukhin
  • Kirill Saskov
  • Alexander Boukhanovsky
  • Wilco Keulen
  • Charles Boucher
  • Peter M. A. Sloot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2657)

Abstract

A multivariate stochastic model for describing the dynamics of complex non-numerical ensembles, such as observed in Human Immunodeficiency Virus (HIV) genome, is developed. This model is based on principle component analyses for numberized variables. The model coefficients are presented in the terms of deterministic trends with correlated lags. The results indicate that we may use this model in short-term forecast of HIV evolution, for evaluation of HIV drug resistance and for testing and validation of diagnostic expert rules. The model also reproduces the specific shape of the bi-modal distribution for the mutations number.

Keywords

Human Immunodeficiency Virus Temporal Variability Cellular Automaton Empirical Orthogonal Function Principle Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ilia Kiryukhin
    • 1
  • Kirill Saskov
    • 1
  • Alexander Boukhanovsky
    • 1
  • Wilco Keulen
    • 2
  • Charles Boucher
    • 3
  • Peter M. A. Sloot
    • 4
  1. 1.Institute for High Performance Computing and Information SystemsSt.PetersburgRussia
  2. 2.Virology NetworkUtrechtThe Netherlands
  3. 3.University Medical CenterUtrechtThe Netherlands
  4. 4.University of AmsterdamAmsterdamThe Netherlands

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