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

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

19 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages285-296
Number of pages12
Volume3721 LNAI
DOIs
Publication statusPublished - 2005
Event9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005 - Porto, Portugal
Duration: Oct 3 2005Oct 7 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3721 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005
CountryPortugal
CityPorto
Period10/3/0510/7/05

Fingerprint

Drug Resistance
Cluster analysis
Support vector machines
Cluster Analysis
Ranking
Mutation
Genes
HIV
Clustering
Scaling
Pharmaceutical Preparations
Domain Knowledge
Pathway
Treatment Failure
Genotype
Phenotype
Bootstrap
Mining
Support Vector Machine
Genome

Keywords

  • Clustering
  • Feature ranking
  • HIV
  • Multidimensional scaling
  • Support vector machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Sing, T., Svicher, V., Beerenwinkel, N., Ceccherini-Silberstein, F., Däumer, M., Kaiser, R., ... Lengauer, T. (2005). Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-based feature ranking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3721 LNAI, pp. 285-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3721 LNAI). https://doi.org/10.1007/11564126_30

Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-based feature ranking. / Sing, Tobias; Svicher, Valentina; Beerenwinkel, Niko; Ceccherini-Silberstein, Francesca; Däumer, Martin; Kaiser, Rolf; Walter, Hauke; Korn, Klaus; Hoffmann, Daniel; Oette, Mark; Rockstroh, Jürgen K.; Fätkenheuer, Gert; Perno, Carlo Federico; Lengauer, Thomas.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3721 LNAI 2005. p. 285-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3721 LNAI).

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

Sing, T, Svicher, V, Beerenwinkel, N, Ceccherini-Silberstein, F, Däumer, M, Kaiser, R, Walter, H, Korn, K, Hoffmann, D, Oette, M, Rockstroh, JK, Fätkenheuer, G, Perno, CF & Lengauer, T 2005, Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-based feature ranking. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3721 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3721 LNAI, pp. 285-296, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005, Porto, Portugal, 10/3/05. https://doi.org/10.1007/11564126_30
Sing T, Svicher V, Beerenwinkel N, Ceccherini-Silberstein F, Däumer M, Kaiser R et al. Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-based feature ranking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3721 LNAI. 2005. p. 285-296. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11564126_30
Sing, Tobias ; Svicher, Valentina ; Beerenwinkel, Niko ; Ceccherini-Silberstein, Francesca ; Däumer, Martin ; Kaiser, Rolf ; Walter, Hauke ; Korn, Klaus ; Hoffmann, Daniel ; Oette, Mark ; Rockstroh, Jürgen K. ; Fätkenheuer, Gert ; Perno, Carlo Federico ; Lengauer, Thomas. / Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-based feature ranking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3721 LNAI 2005. pp. 285-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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