Evolutionary optimization of sequence kernels for detection of bacterial gene starts

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Standard

Evolutionary optimization of sequence kernels for detection of bacterial gene starts. / Mersch, Britta; Glasmachers, Tobias; Meinicke, Peter; Igel, Christian.

I: International Journal of Neural Systems, Bind 17, Nr. 5, 2007, s. 369-381.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Mersch, B, Glasmachers, T, Meinicke, P & Igel, C 2007, 'Evolutionary optimization of sequence kernels for detection of bacterial gene starts', International Journal of Neural Systems, bind 17, nr. 5, s. 369-381. https://doi.org/10.1142/S0129065707001214

APA

Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C. (2007). Evolutionary optimization of sequence kernels for detection of bacterial gene starts. International Journal of Neural Systems, 17(5), 369-381. https://doi.org/10.1142/S0129065707001214

Vancouver

Mersch B, Glasmachers T, Meinicke P, Igel C. Evolutionary optimization of sequence kernels for detection of bacterial gene starts. International Journal of Neural Systems. 2007;17(5):369-381. https://doi.org/10.1142/S0129065707001214

Author

Mersch, Britta ; Glasmachers, Tobias ; Meinicke, Peter ; Igel, Christian. / Evolutionary optimization of sequence kernels for detection of bacterial gene starts. I: International Journal of Neural Systems. 2007 ; Bind 17, Nr. 5. s. 369-381.

Bibtex

@article{50a314fb359b45d4bd711213cc9a2d92,
title = "Evolutionary optimization of sequence kernels for detection of bacterial gene starts",
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 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.",
author = "Britta Mersch and Tobias Glasmachers and Peter Meinicke and Christian Igel",
year = "2007",
doi = "10.1142/S0129065707001214",
language = "English",
volume = "17",
pages = "369--381",
journal = "International Journal of Neural Systems",
issn = "0129-0657",
publisher = "World Scientific Publishing Co. Pte. Ltd.",
number = "5",

}

RIS

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