We identified the genomic regions potentially involved in domestication using standard outlier statistics and tested whether the same genomic regions were identified in different domesticated strains using a kernel-smoothing approach.
First, crash clusters were investigated using a kernel density estimation (KDE) approach.
Moving averages of pairwise F ST values between each hatchery line and the P1 founders at mapped markers were calculated using a kernel smoothing sliding window approach (Hohenlohe et al. 2010; Brieuc et al. 2015).
Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique.
Distributions were estimated using a kernel density distribution.
In this study, we propose a novel bidirectional approach using a kernel-based method, kernel CCA (kernel canonical correlation analysis), to analyze the relationship between regulatory sequences and gene expression profiles [ 15- 17].
We implemented the RKHS regression using a multi-kernel approach, termed kernel averaging (KA), described by de los Campos et al. (2010).
In addition, VQ_fk_nps uses a kernel-based approach to produce the clusters with arbitrary shapes while those algorithms used a partitioning-based approach forming only the hyper-spherical shapes of the resulting clusters.
This approach is similar to the approach in [ 28, 32, 71], where a fixation- density map is calculated using a kernel-density estimation with Gaussian kernels.
This paper proposes a systematic approach for novelty detection of mechanical components, using support vector data description (SVDD), a kernel approach for modeling the support of a distribution.
