Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-Based feature ranking

Authors:
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
Affiliations:
Max Planck Institute for Informatics, Saarbrücken, Germany;University of Rome “Tor Vergata”, Italy;University of California, Berkeley, CA;University of Rome “Tor Vergata”, Italy;University of Cologne, Germany;University of Cologne, Germany;University of Erlangen-Nürnberg, Germany;University of Erlangen-Nürnberg, Germany;Center for Advanced European Studies and Research, Bonn, Germany;University of Düsseldorf, Germany;University of Bonn, Germany;University of Cologne, Germany;University of Rome “Tor Vergata”, Italy;Max Planck Institute for Informatics, Saarbrücken, Germany
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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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.