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Author, Editor

Author(s):

Sing, Tobias
Beerenwinkel, Niko

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dblp

Not MPG Author(s):

Beerenwinkel, Niko

Editor(s):

Schölkopf, B
Platt, J.
Hoffman, T.

dblp
dblp
dblp

Not MPII Editor(s):

Schölkopf, B
Platt, J.
Hoffman, T.

BibTeX cite key*:

SingNIPS2006

Title, Booktitle

Title*:

Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype


SingNIPS2006.pdf (100.64 KB)

Booktitle*:

Advances in Neural Information Processing Systems 19

Event, URLs

URL of the conference:

http://www.nips.cc/Conferences/2006/

URL for downloading the paper:

http://books.nips.cc/papers/files/nips19/NIPS2006_0590.pdf

Event Address*:

Vancouver, B.C., Canada

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

4 December 2006

Event End Date:

7 December 2006

Publisher

Name*:

MIT

URL:


Address*:

Cambridge, MA, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

1-9

Year*:

2007

VG Wort Pages:


ISBN/ISSN:


Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

Starting with the work of Jaakkola and Haussler, a variety of approaches have been proposed for coupling domain-specific generative models with statistical learning methods. The link is established by a kernel function which provides a similarity measure based inherently on the underlying model. In computational biology, the full promise of this framework has rarely ever been exploited, as most kernels are derived from very generic models, such as sequence profiles or hidden Markov models. Here, we introduce the MTreeMix kernel, which is based on a generative model tailored to the underlying biological mechanism. Specifically, the kernel quantifies the similarity of evolutionary escape from antiviral drug pressure between two viral sequence samples. We compare this novel kernel to a standard, evolution-agnostic amino acid encoding in the prediction of HIV drug resistance from genotype, using support vector regression. The results show significant improvements in predictive performance across 17 anti-HIV drugs. Thus, in our study, the generative-discriminative paradigm is key to bridging the gap between population genetic modeling and clinical decision making.



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Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

Audience:

popular

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{SingNIPS2006,
AUTHOR = {Sing, Tobias and Beerenwinkel, Niko},
EDITOR = {Sch{\"o}lkopf, B and Platt, J. and Hoffman, T.},
TITLE = {Mutagenetic tree {F}isher kernel improves prediction of {HIV} drug resistance from viral genotype},
BOOKTITLE = {Advances in Neural Information Processing Systems 19},
PUBLISHER = {MIT},
YEAR = {2007},
PAGES = {1--9},
ADDRESS = {Vancouver, B.C., Canada},
}


Entry last modified by Elena Zotenko, 04/01/2009
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Editor(s)
Andre Altmann
Created
02/20/2007 04:36:34 PM
Revisions
6.
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2.
Editor(s)
Elena Zotenko
Uwe Brahm
Christine Kiesel
Christine Kiesel
Christine Kiesel
Edit Dates
04/01/2009 03:39:43 PM
2007-07-19 15:30:37
03.07.2007 16:30:22
03.07.2007 16:29:22
03.07.2007 16:12:32
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