Characterization of Novel HIV Drug Resistance Mutations Using Clustering, Multidimensional Scaling and SVM-Based Feature Ranking

  • Tobias Sing
  • Valentina Svicher
  • Niko Beerenwinkel
  • Francesca Ceccherini-Silberstein
  • Martin Däumer
  • Rolf Kaiser
  • Hauke Walter
  • Klaus Korn
  • Daniel Hoffmann
  • Mark Oette
  • Jürgen K. Rockstroh
  • Gert Fätkenheuer
  • Carlo-Federico Perno
  • Thomas Lengauer
Conference paper

DOI: 10.1007/11564126_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)
Cite this paper as:
Sing T. et al. (2005) Characterization of Novel HIV Drug Resistance Mutations Using Clustering, Multidimensional Scaling and SVM-Based Feature Ranking. In: Jorge A.M., Torgo L., Brazdil P., Camacho R., Gama J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science, vol 3721. Springer, Berlin, Heidelberg

Abstract

We present a case study on the discovery of clinically relevant domain knowledge in the field of HIV drug resistance. Novel mutations in the HIV genome associated with treatment failure were identified by mining a relational clinical database. Hierarchical cluster analysis suggests that two of these mutations form a novel mutational complex, while all others are involved in known resistance-conferring evolutionary pathways. The clustering is shown to be highly stable in a bootstrap procedure. Multidimensional scaling in mutation space indicates that certain mutations can occur within multiple pathways. Feature ranking based on support vector machines and matched genotype-phenotype pairs comprehensively reproduces current domain knowledge. Moreover, it indicates a prominent role of novel mutations in determining phenotypic resistance and in resensitization effects. These effects may be exploited deliberately to reopen lost treatment options. Together, these findings provide valuable insight into the interpretation of genotypic resistance tests.

Keywords

HIV clustering multidimensional scaling support vector machines feature ranking 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tobias Sing
    • 1
  • Valentina Svicher
    • 2
  • Niko Beerenwinkel
    • 3
  • Francesca Ceccherini-Silberstein
    • 2
  • Martin Däumer
    • 4
  • Rolf Kaiser
    • 4
  • Hauke Walter
    • 5
  • Klaus Korn
    • 5
  • Daniel Hoffmann
    • 6
  • Mark Oette
    • 7
  • Jürgen K. Rockstroh
    • 8
  • Gert Fätkenheuer
    • 4
  • Carlo-Federico Perno
    • 2
  • Thomas Lengauer
    • 1
  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany
  2. 2.University of Rome “Tor Vergata”Italy
  3. 3.University of CaliforniaBerkeleyUSA
  4. 4.University of CologneGermany
  5. 5.University of Erlangen-NürnbergGermany
  6. 6.Center for Advanced European Studies and ResearchBonnGermany
  7. 7.University of DüsseldorfGermany
  8. 8.University of BonnGermany

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