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Author, Editor

Author(s):

Bogojeska, Jasmina
Stöckel, Daniel
Zazzi, Maurizio
Kaiser, Rolf
Incardona, Francesca
Rosen-Zvi, Michal
Lengauer, Thomas

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Not MPG Author(s):

Stöckel, Daniel
Zazzi, Maurizio
Kaiser, Rolf
Incardona, Francesca
Rosen-Zvi, Michal

Editor(s):

Lawrence, Neil
Girolami, Mark

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Not MPII Editor(s):

Lawrence, Neil
Girolami, Mark

BibTeX cite key*:

Bogojeska2012b

Title, Booktitle

Title*:

History-alignment models for bias-aware prediction of virological response to HIV combination therapy

Booktitle*:

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012)

Event, URLs

URL of the conference:

http://www.cs.utexas.edu/~aistats/aistats2012/

URL for downloading the paper:

http://jmlr.csail.mit.edu/proceedings/papers/v22/bogojeska12/bogojeska12.pdf

Event Address*:

La Palma, Canary Islands, Spain

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

21 April 2012

Event End Date:

23 April 2012

Publisher

Name*:

Journal of Machine Learning Research

URL:

http://jmlr.csail.mit.edu/

Address*:

Brookline, USA

Type:


Vol, No, Year, pp.

Series:

JMLR Workshop and Conference Proceedings

Volume:

22

Number:


Month:

April

Pages:

118-126

Year*:

2012

VG Wort Pages:


ISBN/ISSN:


Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

The relevant HIV data sets used for predicting
outcomes of HIV combination therapies
suffer from several problems: different treatment
backgrounds of the samples, uneven
representation with respect to the level of
therapy experience and uneven therapy representation.
Also, they comprise only viral
strain(s) that can be detected in the patients’
blood serum. The approach presented in this
paper tackles these issues by considering not
only the most recent therapies but also the
different treatment backgrounds of the samples
making up the clinical data sets when
predicting the outcomes of HIV therapies.
For this purpose, we introduce a similarity measure for sequences of therapies and use
it for training separate linear models for predicting
therapy outcome for each target sample.
Compared to the most commonly used
approach that encodes all available treatment
information only by specific input features
our approach has the advantage of delivering
significantly more accurate predictions
for therapy-experienced patients and for rare
therapies. Additionally, the sample-specific
models are more interpretable which is very
important in medical applications.



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:

@INPROCEEDINGS{Bogojeska2012b,
AUTHOR = {Bogojeska, Jasmina and St{\"o}ckel, Daniel and Zazzi, Maurizio and Kaiser, Rolf and Incardona, Francesca and Rosen-Zvi, Michal and Lengauer, Thomas},
EDITOR = {Lawrence, Neil and Girolami, Mark},
TITLE = {History-alignment models for bias-aware prediction of virological response to {HIV} combination therapy},
BOOKTITLE = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012)},
PUBLISHER = {Journal of Machine Learning Research},
YEAR = {2012},
VOLUME = {22},
PAGES = {118--126},
SERIES = {JMLR Workshop and Conference Proceedings},
ADDRESS = {La Palma, Canary Islands, Spain},
MONTH = {April},
ISBN = {1532-4435},
}


Entry last modified by Uwe Brahm, 02/15/2013
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Editor(s)
[Library]
Created
12/12/2012 01:51:29 PM
Revisions
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Editor(s)
Uwe Brahm
Anja Becker
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Edit Dates
02/15/2013 04:14:27 PM
11.02.2013 14:47:20
10.01.2013 11:43:54
10.01.2013 11:09:13
12/12/2012 01:51:29 PM