Engineering support vector machine kernels that recognize translation initiation sites

A. Zien; G. Rätsch; S. Mika; B. Schölkopf; T. Lengauer; K. R. Müller

doi:10.1093/bioinformatics/16.9.799

Engineering support vector machine kernels that recognize translation initiation sites

A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer, K. R. Müller

Research output: Contribution to journal › Article › peer-review

327 Citations (Scopus)

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.

Original language	English
Pages (from-to)	799-807
Number of pages	9
Journal	Bioinformatics
Volume	16
Issue number	9
DOIs	https://doi.org/10.1093/bioinformatics/16.9.799
Publication status	Published - 2000

Bibliographical note

Funding Information:
This work was supported by the BMBF (TargId, 0311615) and by the DFG (JA 379/9-1,7-1). Part of the present work was done while BS was with GMD.FIRST. We thank A. G. Pedersen and H. Nielsen for e-mail discussions, providing their data sets and sharing unpublished data. We acknowledge M. Schwan for help with the experiments. We thank the referees for helpful comments.

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/16.9.799

Cite this

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title = "Engineering support vector machine kernels that recognize translation initiation sites",

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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note = "Funding Information: This work was supported by the BMBF (TargId, 0311615) and by the DFG (JA 379/9-1,7-1). Part of the present work was done while BS was with GMD.FIRST. We thank A. G. Pedersen and H. Nielsen for e-mail discussions, providing their data sets and sharing unpublished data. We acknowledge M. Schwan for help with the experiments. We thank the referees for helpful comments.",

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doi = "10.1093/bioinformatics/16.9.799",

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AU - Zien, A.

AU - Rätsch, G.

AU - Mika, S.

AU - Schölkopf, B.

AU - Lengauer, T.

AU - Müller, K. R.

N1 - Funding Information: This work was supported by the BMBF (TargId, 0311615) and by the DFG (JA 379/9-1,7-1). Part of the present work was done while BS was with GMD.FIRST. We thank A. G. Pedersen and H. Nielsen for e-mail discussions, providing their data sets and sharing unpublished data. We acknowledge M. Schwan for help with the experiments. We thank the referees for helpful comments.

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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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Engineering support vector machine kernels that recognize translation initiation sites

Abstract

Bibliographical note

ASJC Scopus subject areas

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