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  Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A., et al. (2000). Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. Bioinformatics, 16(9), 799-807. doi:10.1093/bioinformatics/16.9.799.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E64F-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-C556-C
Genre: Conference Paper

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 Creators:
Zien, A, Author              
Rätsch, G, Author              
Mika, S, Author
Schölkopf, B1, Author              
Lemmen, C, Author
Smola, AJ, Author              
Lengauer, T, Author
Müller, K-R, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

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 Dates: 1999-102000-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5046
DOI: 10.1093/bioinformatics/16.9.799
 Degree: -

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Title: German Conference on Bioinformatics (GCB 1999)
Place of Event: Hannover, Germany
Start-/End Date: 1999-10-04 - 1999-10-06

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Title: Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 16 (9) Sequence Number: - Start / End Page: 799 - 807 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991