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          Institute: MPI für Informatik     Collection: Computational Biology and Applied Algorithmics     Display Documents



ID: 356656.0, MPI für Informatik / Computational Biology and Applied Algorithmics
Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance
Authors:Altmann, Andre; Beerenwinkel, Niko; Sing, Tobias; Savenkov, Igor; Däumer, Martin; Kaiser, Rolf; Rhee, Soo-Yon; Fessel, W Jeffrey; Shafer, Robert W; Lengauer, Thomas
Language:English
Date of Publication (YYYY-MM-DD):2007
Title of Journal:Antiviral Therapy
Volume:12
Issue / Number:2
Start Page:169
End Page:178
Review Status:Peer-review
Audience:Experts Only
Intended Educational Use:No
Abstract / Description:Background: The outcome of antiretroviral combination therapy depends on many
factors involving host, virus, and drugs. We investigate prediction of
treatment response from the applied drug combination and the genetic
constellation of the virus population at baseline. The virus’s evolutionary
potential for escaping from drug pressure is explored as an additional
predictor.
Methods: We compare different encodings of the viral genotype and
antiretroviral regimen including phenotypic and evolutionary information,
namely predicted phenotypic drug resistance, activity of the regimen estimated
from sequence space search, the genetic barrier to drug resistance, and the
genetic progression score. These features were evaluated in the context of
different statistical learning procedures applied to the binary classification
task of predicting virological response. Classifier performance was evaluated
using cross-validation and receiver operating characteristic curves on 6,337
observed treatment change episodes from the Stanford HIV Drug Resistance
Database and a large US clinic-based patient population.
Results: We find that the choice of appropriate features affects predictive
performance more profoundly than the choice of the statistical learning method.
Application of the genetic barrier to drug resistance, which combines
phenotypic and evolutionary information, outperformed the genetic progression
score, which uses exclusively evolutionary knowledge. The benefit of phenotypic
information in predicting virological response was confirmed by using predicted
fold changes in drug susceptibility. Moreover, genetic barrier and predicted
phenotypic drug resistance were found to be the best encodings across all
datasets and statistical learning methods examined.
Availability: THEO (THErapy Optimizer), a prototypical implementation of the
best performing approach, is freely available for research purposes at
http://www.geno2pheno.org.
Last Change of the Resource (YYYY-MM-DD):2008-02-28
External Publication Status:published
Document Type:Article
Communicated by:Thomas Lengauer
Affiliations:MPI für Informatik/Computational Biology and Applied Algorithmics
Identifiers:LOCALID:C12573CC004A8E26-6640B953DA2CDA2DC1257288005464A4-...
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