The approach consists of de-regressing all GEBV on which pre-selection was based, using the effective contribution of the additional genomic information as the weight.
The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish.
Labor outperforms the other parties with their representation of women, largely thanks to a rule implemented in 2012 which requires pre-selections result in at least 40% of Labor seats being held by women.
In addition, our k-mer repeat maps correlated well with their equivalent "ambiguous reads" maps, for which no pre-selection process had been done prior to sequencing; this further supports our finding that genome regions with k-mer repeats tend to be more highly methylated than regions with uniquely mapped reads.
Taken together, a sensitive and donor-independent assay was established to measure T cell activation upon exposure to patient-derived tumor tissue, which may facilitate pre-selection of patients for clinical adoptive T cell therapy.
MDI is particularly appropriate for applications in which a gene pre-selection step is performed (e.g. on the basis of differential expression).
Especially, SNF is demonstrated to outperform other integrative methods like iCluster [ 31] which is based on pre-selection of genes.
The current pick-up rate based on UK referrals in the Exeter genetics diagnostic laboratory is 27% [ 3], which is based on pre-selection of patients by healthcare professionals in line with best-practice guidelines [ 14].
The result in Figure 3(b) is even worse than that obtained by BW, which shows that feature pre-selection and sample clustering can really improve the performance of ANMM in noisy data.
Tables 4 and 5 together show an interaction between marker density and level of pre-selection, which indicates that marker density is more relevant when more animals are genotyped and that level of pre-selection is also more relevant for higher marker densities.
Our main goal in this paper is to understand parallel performance of hyperspectral imaging algorithms comprising the standard hyperspectral data processing chain (which includes pre-processing, selection of pure spectral components and linear spectral unmixing) in the context of fully heterogeneous computing platforms.
