Given the high dimensionality of real-world effects on the probability distribution of lightness/brightness sources, it seems inevitable that the relevant statistical instantiations entail the conditional probabilities of concatenations of luminance values in retinal images with respect to the underlying natural sources.
Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS BC pair is enriched in a list of gene QT findings.
Given the high dimensionality of the proposed feature prototype and the different choices, building the proposed model with the different parameters for model selection demanded heavy computation.
The Pearson correlation coefficient [22] was computed between all gene pairs and, given the high dimensionality of the obtained correlation matrix, three aggregation criteria were used for analysis.
However, given the high dimensionality of the parameters (e.g. and α), it would take a long time for a sampler to converge.
Given the high dimensionality of genotypic (e.g. SNP) data, these studies are limited by computational resources and statistical power to searching for binary gene-gene interactions.
Given the high dimensionality of the model, one may think that it is relatively easy to capture the biological behavior of the majority of the mutants [ 30].
Given the high dimensionality of the conformational space and the large entropy of the denatured state, saturation could only be achieved by extremely long MD simulations far from the reach of current computer power.
Given the very high dimensionality of the VBM output (thousands of voxels, or features, for each subject, each one corresponding to one dimension) and the expectation that only a few of these features would be meaningful for prediction, we applied a further feature selection step [36].
Such simplified distance measures are necessary to guarantee scalability given the extremely high dimensionality of the k-mer features.
