Evolutionary optimization of sequence kernels for detection of bacterial gene starts
2006 | conference paper. A publication with affiliation to the University of Göttingen.
Jump to: Cite & Linked | Documents & Media | Details | Version history
Cite this publication
Mersch, B., Glasmachers, T., Meinicke, P. & Igel, C. (2006). Evolutionary optimization of sequence kernels for detection of bacterial gene starts. In S. Kollias (Ed.), Artificial neural networks - ICANN 2006 , 2. doi: https://doi.org/10.1007/11840930_86
Documents & Media
Details
- Authors
- Mersch, Britta; Glasmachers, Tobias; Meinicke, Peter ; Igel, Christian
- Editors
- Kollias, Stefanos
- Abstract
- Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernel for the detection of prokaryotic translation initiation sites. The resulting kernel leads to higher classification rates, and the adapted parameters reveal the importance for classification of particular triplets, for example of those occurring in the Shine-Dalgarno sequence.
- Issue Date
- 2006
- Publisher
- Springer
- Conference
- 16th International Conference on Artificial Neural Networks (ICANN 2006)
- Series
- Lecture Notes in Computer Science
- ISBN
- 3-540-38871-0
978-3-540-38873-9 - Conference Place
- Athens
- Event start
- 2006-09-10
- Event end
- 2006-09-14
- ISSN
- 0302-9743
- Language
- English