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Author, Editor(s)

Author(s):

Bogojeska, Jasmina
Bickel, Steffen
Altmann, André
Lengauer, Thomas

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Not MPG Author(s):

Bickel, Steffen

BibTeX cite key*:

Bogojeska2010

Title

Title*:

Dealing with sparse data in predicting outcomes of HIV combination therapies

Journal

Journal Title*:

Bioinformatics

Journal's URL:

http://bioinformatics.oxfordjournals.org/

Download URL
for the article:

http://bioinformatics.oxfordjournals.org/content/26/17/2085.full

Language:

English

Publisher

Publisher's
Name:

Oxford University Press

Publisher's URL:

http://www.oup.com/

Publisher's
Address:

Oxford, UK

ISSN:

1367-4803

Vol, No, pp, Date

Volume*:

26

Number:

17

Publishing Date:

July 2010

Pages*:

2085-2092

Number of
VG Pages:

8

Page Start:

2085

Page End:

2092

Sequence Number:


DOI:

10.1093/bioinformatics/btq361

Note, Abstract, ©

Note:


(LaTeX) Abstract:

Motivation: As there exists no cure or vaccine for the infection with human immunodeficiency virus (HIV), the standard approach to treating HIV patients is to repeatedly administer different combinations of several antiretroviral drugs. Because of the large number of possible drug combinations, manually finding a successful regimen becomes practically impossible. This presents a major challenge for HIV treatment. The application of machine learning methods for predicting virological responses to potential therapies is a possible approach to solving this problem. However, due to evolving trends in treating HIV patients the available clinical datasets have a highly unbalanced representation, which might negatively affect the usefulness of derived statistical models.

Results: This article presents an approach that tackles the problem of predicting virological response to combination therapies by learning a separate logistic regression model for each therapy. The models are fitted by using not only the data from the target therapy but also the information from similar therapies. For this purpose, we introduce and evaluate two different measures of therapy similarity. The models are also able to incorporate phenotypic knowledge on the therapy outcomes through a Gaussian prior. With our approach we balance the uneven therapy representation in the datasets and produce higher quality models for therapies with very few training samples. According to the results from the computational experiments our therapy similarity model performs significantly better than training separate models for each therapy by using solely their examples. Furthermore, the model's performance is as good as an approach that encodes therapy information in the input feature space with the advantage of delivering better results for therapies with very few training samples.

URL for the Abstract:

http://bioinformatics.oxfordjournals.org/content/26/17/2085.abstract

Categories,
Keywords:

Statistical learning, HIV

HyperLinks / References / URLs:


Copyright Message:

Copyright © 2010 Oxford University Press

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:

@ARTICLE{Bogojeska2010,
AUTHOR = {Bogojeska, Jasmina and Bickel, Steffen and Altmann, Andr{\'e} and Lengauer, Thomas},
TITLE = {Dealing with sparse data in predicting outcomes of {HIV} combination therapies},
JOURNAL = {Bioinformatics},
PUBLISHER = {Oxford University Press},
YEAR = {2010},
NUMBER = {17},
VOLUME = {26},
PAGES = {2085--2092},
ADDRESS = {Oxford, UK},
MONTH = {July},
ISBN = {1367-4803},
DOI = {10.1093/bioinformatics/btq361},
}


Entry last modified by Anja Becker, 01/19/2011
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Editor(s)
[Library]
Created
12/09/2010 05:18:19 PM
Revisions
5.
4.
3.
2.
1.
Editor(s)
Anja Becker
Anja Becker
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Edit Dates
19.01.2011 15:35:46
14.01.2011 14:23:20
12.01.2011 15:33:55
11.01.2011 15:15:03
12/09/2010 05:19:59 PM