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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84331

Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Mika S, Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84096

Müller,  K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zien, A., Rätsch, G., Mika S, Schölkopf, B., Lemmen C, Smola A, Lengauer, T., & Müller, K.-R. (1999). Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. Talk presented at German Conference on Bioinformatics (GCB‘99). Heidelberg, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E64F-5
Abstract
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro� teins start, the so�called translation initiation sites (TIS). This can be modeled as a classification prob� lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc� cessfully incorporate biological prior knowledge by engineering an appropriate kernel function.