As described in Section 2.4, we create a kernel for every candidate motif ℳ m and use a window of 20 bp around each occurrence in every sequence.
For each vocabulary-based gene-by-term data source, we create a kernel matrix using the linear kernel function and the kernel normalization method proposed by (Shawe-Taylor and Cristianini, 2004), (Chapter 5).
Approaches of the first type create a kernel function from the co-functionality relationships encoded in the network and use this kernel with kernel-based classification algorithms [ 8, 9, 17].
We created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP) and estimated that, in this particular data set, epigenetic variation accounted for 65% of the phenotypic variance.
Finally, we subtracted the kernel density surface of the unhealthful food vendors from the kernel density surface of the healthful food vendors to create a composite kernel density surface.
Our aim was to perform inelastic neutron scattering measurements with sufficient range in energy and momentum to create a scattering kernel to simulate its behavior as a moderator material.
For the specific case of network simulation, a simulator kernel creates a distinct LP as a computational unit for each element of the simulated network model (Nicol and Fujimoto 1994).
The set_source function is then used to assign a 1D Beta model convolved with the file-based PSF kernel to source data set 2. The load_conv function creates a model-based PSF convolution kernel using the Sherpa 1D Gaussian model component 'gauss1d.psf', and assigns it the id 'conv'.
The contour_kernel command creates a contour plot of the kernel image.
This exchange of choice creates a clear gain, as each kernel is adopted in the training necessary to optimize its parameters and in the case of PUK, the kernel is not exchanged for another, because it mimics the behavior of other kernels.
