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  Protein function prediction via graph kernels

Borgwardt, K., Ong, C., Schönauer, S., Vishwanathan, S., Smola, A., & Kriegel, H.-P. (2005). Protein function prediction via graph kernels. Bioinformatics, 21(Supplement 1), i47-i56. doi:10.1093/bioinformatics/bti1007.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D553-F Version Permalink: http://hdl.handle.net/21.11116/0000-0005-38CE-5
Genre: Journal Article

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Borgwardt, KM1, Author              
Ong, CS1, Author              
Schönauer, S, Author
Vishwanathan , SVN, Author
Smola, AJ1, Author              
Kriegel, H-P, Author
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1External Organizations, ou_persistent22              

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 Abstract: Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.

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 Dates: 2005-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1093/bioinformatics/bti1007
BibTex Citekey: 3415
 Degree: -

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 21 (Supplement 1) Sequence Number: - Start / End Page: i47 - i56 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991