Evolutionary optimization of sequence kernels for detection of bacterial gene starts
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Evolutionary optimization of sequence kernels for detection of bacterial gene starts. / Mersch, Britta; Glasmachers, Tobias; Meinicke, Peter; Igel, Christian.
In: International Journal of Neural Systems, Vol. 17, No. 5, 2007, p. 369-381.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Evolutionary optimization of sequence kernels for detection of bacterial gene starts
AU - Mersch, Britta
AU - Glasmachers, Tobias
AU - Meinicke, Peter
AU - Igel, Christian
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
U2 - 10.1142/S0129065707001214
DO - 10.1142/S0129065707001214
M3 - Journal article
C2 - 18098369
VL - 17
SP - 369
EP - 381
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
SN - 0129-0657
IS - 5
ER -
ID: 32645882