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Author, Editor(s)
Author(s):
Altmann, Andre
Beerenwinkel, Niko
Sing, Tobias
Savenkov, Igor
Däumer, Martin
Kaiser, Rolf
Rhee, Soo-Yon
Fessel, W Jeffrey
Shafer, Robert W
Lengauer, Thomas		dblp
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Not MPG Author(s):
Beerenwinkel, Niko
Däumer, Martin
Kaiser, Rolf
Rhee, Soo-Yon
Fessel, W Jeffrey
Shafer, Robert W

BibTeX cite key*:

Altmann2007

Title

Title*:

Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance

Journal

Journal Title*:

Antiviral Therapy
Journal's URL:

http://www.intmedpress.com/General/showSectionSub.cfm?SectionID=2&SectionSubID=1&SectionSubSubID=1

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for the article:


Language:

English

Publisher

Publisher's
Name:

International Medical
Publisher's URL:

http://www.intmedpress.com

Publisher's
Address:

London, UK
ISSN:


Vol, No, pp, Date

Volume*:

12
Number:

2

Publishing Date:

2007

Pages*:

169-178
Number of
VG Pages:


Page Start:

169
Page End:

178

Sequence Number:


DOI:


Note, Abstract, ©

Note:


(LaTeX) Abstract:

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.

URL for the Abstract:

http://www.intmedpress.com/Journal%20Management/article.cfm?viewinfo=3742183F520F092C300C585F144F163F234113213A35431D07421A5525120C534A3601411927654B004A1C5E161E0B155D3D17511379286B00450A171154545E0725550F22580C022C300C5B48721353183F5E0D3F1016782D531050630935450A030B27440B3D541479286B00

Categories,
Keywords:


HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


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Access Level:

Intranet

Correlation
MPG Unit:
Max-Planck-Institut für Informatik
MPG Subunit:
Computational Biology and Applied Algorithmics
Appearance:
MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort


BibTeX Entry:

@ARTICLE{Altmann2007,
AUTHOR = {Altmann, Andre and Beerenwinkel, Niko and Sing, Tobias and Savenkov, Igor and D{\"a}umer, Martin and Kaiser, Rolf and Rhee, Soo-Yon and Fessel, W Jeffrey and Shafer, Robert W and Lengauer, Thomas},
 TITLE = {Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance},
 JOURNAL = {Antiviral Therapy},
 PUBLISHER = {International Medical},
 YEAR = {2007},
 NUMBER = {2},
 VOLUME = {12},
 PAGES = {169--178},
 ADDRESS = {London, UK},
}

Entry last modified by Christine Kiesel, 02/28/2008
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Editor(s)
Andre Altmann		Created
02/20/2007 04:21:47 PM
Revisions
3.
2.
1.
0.
Editor(s)
Christine Kiesel
Andre Altmann
Andre Altmann
Andre Altmann
Edit Dates
01.07.2007 09:04:07
03/23/2007 02:55:29 PM
02/20/2007 04:41:12 PM
02/20/2007 04:21:47 PM