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          Institute: MPI für Informatik     Collection: Computational Biology and Applied Algorithmics     Display Documents



ID: 356657.0, MPI für Informatik / Computational Biology and Applied Algorithmics
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
Authors:Sing, Tobias; Beerenwinkel, Niko
Language:English
Publisher:MIT
Place of Publication:Cambridge, MA, USA
Date of Publication (YYYY-MM-DD):2007
Title of Proceedings:Advances in Neural Information Processing Systems 19 : [proceedings of the 20th Conference on Advances in Neural Information Porcessing Systems (NIPS)]
Start Page:1297
End Page:1304
Title of Series:Advances in Neural Information Processing Systems 19
Place of Conference/Meeting:Vancouver, B.C., Canada
(Start) Date of Conference/Meeting
 (YYYY-MM-DD):
2006-12-04
End Date of Conference/Meeting 
 (YYYY-MM-DD):
2006-12-07
Audience:Experts Only
Intended Educational Use:No
Abstract / Description: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.
Last Change of the Resource (YYYY-MM-DD):2008-03-12
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Thomas Lengauer
Affiliations:MPI für Informatik/Computational Biology and Applied Algorithmics
Identifiers:LOCALID:C12573CC004A8E26-845535906AEB88E6C12572880055BEE4-...
ISBN:0-262-19568-2
Full Text:
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