Journal Article
@Article
Artikel in Fachzeitschrift


Show entries of:

this year (2019) | last year (2018) | two years ago (2017) | Notes URL

Action:

login to update

Options:




Library Locked Library locked




Author, Editor(s)

Author(s):

Bogojeska, Jasmina
Lengauer, Thomas

dblp
dblp



BibTeX cite key*:

Bogojeska2012a

Title

Title*:

Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies

Journal

Journal Title*:

Statistical Applications in Genetics and Molecular Biology

Journal's URL:

http://www.degruyter.com/view/j/sagmb

Download URL
for the article:


Language:

English

Publisher

Publisher's
Name:

De Gruyter

Publisher's URL:

http://www.degruyter.com/

Publisher's
Address:

Boston, MA

ISSN:

1554-6115

Vol, No, pp, Date

Volume*:

11

Number:

3

Publishing Date:

April 2012

Pages*:

11.1-11.19

Number of
VG Pages:


Page Start:

11.1

Page End:

11.19

Sequence Number:

11

DOI:

10.1515/1544-6115.1769

Note, Abstract, ©

Note:


(LaTeX) Abstract:

HIV patients are treated by administration of combinations of antiretroviral drugs. The very large number of such combinations makes the manual search for an effective therapy practically impossible, especially in advanced stages of the disease. Therapy selection can be supported by statistical methods that predict the outcomes of candidate therapies. However, these methods are based on clinical data sets that have highly unbalanced therapy representation.This paper presents a novel approach that considers each drug belonging to a target combination therapy as a separate task in a multi-task hierarchical Bayes setting. The drug-specific models take into account information on all therapies containing the drug, not just the target therapy. In this way, we can circumvent the problem of data sparseness pertaining to some target therapies.The computational validation shows that compared to the most commonly used approach that provides therapy information in the form of input features, our model has significantly higher predictive power for therapies with very few training samples and is at least as powerful for abundant therapies.

URL for the Abstract:

http://www.degruyter.com/view/j/sagmb.2012.11.issue-3/1544-6115.1769/1544-6115.1769.xml?rskey=JoFMe2&result=1&q=Bogojeska

Categories,
Keywords:

hierarchical Bayes modelling, HIV combination therapies, statistical models, classification

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:

@ARTICLE{Bogojeska2012a,
AUTHOR = {Bogojeska, Jasmina and Lengauer, Thomas},
TITLE = {Hierarchical {Bayes} Model for Predicting Effectiveness of {HIV} Combination Therapies},
JOURNAL = {Statistical Applications in Genetics and Molecular Biology},
PUBLISHER = {De Gruyter},
YEAR = {2012},
NUMBER = {3},
VOLUME = {11},
PAGES = {11.1--11.19},
ADDRESS = {Boston, MA},
MONTH = {April},
ISBN = {1554-6115},
DOI = {10.1515/1544-6115.1769},
}


Entry last modified by Anja Becker, 03/13/2013
Show details for Edit History (please click the blue arrow to see the details)Edit History (please click the blue arrow to see the details)
Hide details for Edit History (please click the blue arrow to see the details)Edit History (please click the blue arrow to see the details)

Editor(s)
[Library]
Created
12/12/2012 01:36:24 PM
Revisions
4.
3.
2.
1.
0.
Editor(s)
Anja Becker
Anja Becker
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Edit Dates
13.03.2013 09:54:16
11.02.2013 14:39:23
10.01.2013 11:20:44
10.01.2013 11:08:31
12/12/2012 01:36:24 PM