Evolutionary optimization of sequence kernels for detection of bacterial gene starts

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Britta Mersch, Tobias Glasmachers, Peter Meinicke, Christian Igel

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 kernels for the detection of bacterial gene starts. The resulting kernels lead to higher classification rates, and the adapted parameters reveal the importance of particular triplets for classification, for example of those occurring in the Shine-Dalgarno Sequence.
TidsskriftInternational Journal of Neural Systems
Udgave nummer5
Sider (fra-til)369-381
Antal sider13
StatusUdgivet - 2007
Eksternt udgivetJa

ID: 32645882