Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection

Igel C.; Glasmachers T.; Mersch B.; Pfeifer N.; Meinicke P.

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Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection

机译：基于梯度优化的核仁序列比对应用于细菌基因启动检测的序列核

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Biological data mining using kernel methods can be improved by a task-specific choice of the kernel function. Oligo kernels for genomic sequence analysis have proven to have a high discriminative power and to provide interpretable results. Oligo kernels that consider subsequences of different lengths can be combined and parameterized to increase their flexibility. For adapting these parameters efficiently, gradient-based optimization of the kernel-target alignment is proposed. The power of this new, general model selection procedure and the benefits of fitting kernels to problem classes are demonstrated by adapting oligo kernels for bacterial gene start detection

机译：通过任务特定的内核功能选择，可以改善使用内核方法的生物数据挖掘。已经证明，用于基因组序列分析的寡核苷酸具有很高的判别力，并且可以提供可解释的结果。可以将考虑不同长度子序列的Oligo内核进行组合和参数化以增加其灵活性。为了有效地适应这些参数，提出了基于梯度的核-目标比对优化。通过使寡核苷酸适应细菌基因启动检测，证明了这种新的通用模型选择程序的功能以及将内核适合问题类别的好处。

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2007年第2期|p.216-226|共11页
作者
Igel C.; Glasmachers T.; Mersch B.; Pfeifer N.; Meinicke P.;
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正文语种 eng
中图分类生物数学方法;生物信息论;
关键词
biology computing; data mining; genetics; gradient methods; microorganisms; molecular biophysics; molecular configurations; optimisation; bacterial gene start detection; biological data mining; genomic sequence analysis; gradient-based optimization; kernel-target al;

机译：生物学计算;数据挖掘;遗传学;梯度方法;微生物;分子生物物理学;分子构型;优化;细菌基因启动检测;生物数据挖掘;基因组序列分析;基于梯度的优化;核靶标;

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专利

1. EVOLUTIONARY OPTIMIZATION OF SEQUENCE KERNELS FOR DETECTION OF BACTERIAL GENE STARTS [J] . BRITTA MERSCH, TOBIAS GLASMACHERS, PETER MEINICKE, International Journal of Neural Systems . 2007,第5期

机译：用于检测细菌基因起始点的序列核的进化优化
2. A Study on Multi-Scale Kernel Optimisation via Centered Kernel-Target Alignment [J] . Perez-Ortiz M., Gutierrez P. A., Sanchez-Monedero J., Neural processing letters . 2016,第2期

机译：基于中心核目标对齐的多尺度内核优化研究
3. Kernel-target alignment based non-linear metric learning [J] . Xu Yonghui, Miao Chunyan, Liu Yong, Neurocomputing . 2020,第Octa21期

机译：基于内核 - 目标对齐的非线性度量学习
4. Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts [C] . Britta Mersch, Tobias Glasmachers, Peter Meinicke, International Conference on Artificial Neural Networks(ICANN 2006) pt.2; 20060910-14; Athens(GR) . 2006

机译：用于检测细菌基因起始的序列核的进化优化。
5. A Novel Fault Detection and Classification Approach in Semiconductor Manufacturing Using Time Series Alignment Kernel [D] . Zhu, Feng. 2020

机译：使用时间序列对准核的半导体制造的一种新型故障检测和分类方法
6. Bacterial Foraging Optimization –Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives [O] . P. Manikandan, D. Ramyachitra -1

机译：细菌觅食优化–多目标多序列比对的遗传算法
7. Gradient-based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection [O] . Christian Igel, Tobias Glasmachers, Britta Mersch, 2008

机译：应用于细菌基因启动检测的序列核基于核的目标比对的基于梯度的优化

Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection

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