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Author, Editor

Author(s):

Saigo, Hiroto
Altmann, André
Bogojeska, Jasmina
Müller, Fabian
Nowozin, Sebastian
Lengauer, Thomas

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Not MPG Author(s):

Nowozin, Sebastian

BibTeX cite key*:

Saigo2011

Title

Title*:

Learning from past treatments and their outcome improves prediction of In Vivo response to anti-HIV therapy

Journal

Journal Title*:

Statistical Applications in Genetics and Molecular Biology

Journal's URL:

http://www.bepress.com/sagmb/

Download URL
for the article:

http://dx.doi.org/10.2202/1544-6115.1604

Language:

English

Publisher

Publisher's
Name:

The Berkeley Electronic Press

Publisher's URL:

http://www.bepress.com/

Publisher's
Address:

Berkeley, Calif.

ISSN:

1544-6115

Vol, No, Year, pp.

Volume:

10

Number:

1

Month:


Year*:

2011

Pages:

1-32

Number of VG Pages:


Sequence Number:

1

DOI:

10.2202/1544-6115.1604

Abstract, Links, (C)

Note:


(LaTeX) Abstract:

Infections with the human immunodeficiency virus type 1 (HIV-1) are treated with combinations of drugs. Unfortunately, HIV responds to the treatment by developing resistance mutations. Consequently, the genome of the viral target proteins is sequenced and inspected for resistance mutations as part of routine diagnostic procedures for ensuring an effective treatment. For predicting response to a combination therapy, currently available computer-based methods rely on the genotype of the virus and the composition of the regimen as input. However, no available tool takes full advantage of the knowledge about the order of and the response to previously prescribed regimens. The resulting high-dimensional feature space makes existing methods difficult to apply in a straightforward fashion. The machine learning system proposed in this work, sequence boosting, is tailored to exploiting such high-dimensional information, i.e. the extraction of longitudinal features, by utilizing the recent advancements in data mining and boosting.

When applied to predicting the latest treatment outcome for 3,759 treatment-experienced patients from the EuResist integrated database, sequence boosting achieved superior performance compared to SVMs with RBF kernels. Moreover, sequence boosting allows an easy access to the discriminative treatment information.

Analysis of feature importance values provided by our model confirmed known facts regarding HIV treatment. For instance, application of potent and recently licensed drugs was beneficial for patients, and, conversely, the patient group that was subject to NRTI mono-therapies in the past had poor treatment perspectives today. Furthermore, our model revealed novel biological insights. More precisely, the combination of previously used drugs with their in vivo response is more informative than the information of previously used drugs alone. Using this information improves the performance of systems for predicting therapy outcome.

URL for the Abstract:

http://www.bepress.com/sagmb/vol10/iss1/art6/

Categories / Keywords:


HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


Download
Access Level:

Internal

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:

@MISC{Saigo2011,
AUTHOR = {Saigo, Hiroto and Altmann, Andr{\'e} and Bogojeska, Jasmina and M{\"u}ller, Fabian and Nowozin, Sebastian and Lengauer, Thomas},
TITLE = {Learning from past treatments and their outcome improves prediction of In Vivo response to anti-{HIV} therapy},
JOURNAL = {Statistical Applications in Genetics and Molecular Biology},
PUBLISHER = {The Berkeley Electronic Press},
YEAR = {2011},
NUMBER = {1},
VOLUME = {10},
PAGES = {1--32},
ADDRESS = {Berkeley, Calif.},
ISBN = {1544-6115},
DOI = {10.2202/1544-6115.1604},
}


Entry last modified by Anja Becker, 03/20/2012
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Editor(s)
[Library]
Created
01/24/2011 02:17:38 PM
Revisions
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Editor(s)
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Edit Dates
20.03.2012 11:58:52
20.03.2012 11:57:40
27.02.2012 11:59:46
27.02.2012 11:57:47
16.01.2012 11:08:02