Abstract
RNA viruses provide prominent examples of measurably evolving populations. In human immunodeficiency virus (HIV) infection, the development of drug resistance is of particular interest because precise predictions of the outcome of this evolutionary process are a prerequisite for the rational design of antiretroviral treatment protocols. We present a mutagenetic tree hidden Markov model for the analysis of longitudinal clonal sequence data. Using HIV mutation data from clinical trials, we estimate the order and rate of occurrence of seven amino acid changes that are associated with resistance to the reverse transcriptase inhibitor efavirenz.
| Original language | English |
|---|---|
| Pages (from-to) | 53-71 |
| Number of pages | 19 |
| Journal | Biostatistics |
| Volume | 8 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2007 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- EM algorithm
- Graphical model
- HIV drug resistance
- Hidden Markov model
- Longitudinal data
- Measurably evolving populations
- Mutagenetic tree
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