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 language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 285-296 |
| Number of pages | 12 |
| Volume | 3721 LNAI |
| DOIs | |
| Publication status | Published - 2005 |
| Event | 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005 - Porto, Portugal Duration: Oct 3 2005 → Oct 7 2005 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3721 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005 |
|---|---|
| Country | Portugal |
| City | Porto |
| Period | 10/3/05 → 10/7/05 |
Fingerprint
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
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 proceeding › Conference contribution
}
TY - GEN
T1 - Characterization of novel HIV drug resistance mutations using clustering, multidimensional scaling and SVM-based feature ranking
AU - Sing, Tobias
AU - Svicher, Valentina
AU - Beerenwinkel, Niko
AU - Ceccherini-Silberstein, Francesca
AU - Däumer, Martin
AU - Kaiser, Rolf
AU - Walter, Hauke
AU - Korn, Klaus
AU - Hoffmann, Daniel
AU - Oette, Mark
AU - Rockstroh, Jürgen K.
AU - Fätkenheuer, Gert
AU - Perno, Carlo Federico
AU - Lengauer, Thomas
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Clustering
KW - Feature ranking
KW - HIV
KW - Multidimensional scaling
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=33646418385&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646418385&partnerID=8YFLogxK
U2 - 10.1007/11564126_30
DO - 10.1007/11564126_30
M3 - Conference contribution
AN - SCOPUS:33646418385
SN - 3540292446
SN - 9783540292449
VL - 3721 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 285
EP - 296
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -