Although none of the discretization methods was able to reproduce every observed pattern, we selected the quantile binary discretization Q2 as the method that best captures some important dynamical features in the data.
Considering measures that use both Information-theoretic and class-based features to determine the significance of a pattern, we selected two measures that are popular in the machine learning and data mining communities: the J-Measure (J) [ 44] and the Mutual Information (I), which is derived from the Shannon's entropy theory [ 34, 45, 46].
Based on predominantly A expression patterns, we selected four genes, Sb, CG16884, CG15905, and CG10200, for further analysis.
In order to distinguish groups of patients according to their DNA methylation patterns, we selected only the informative probes, that is, considering those probes with SD > 0.05 between patients.
For distinct pattern identification, we selected those patterns that are only occurring in one network as modules.
To illustrate the relationship of the activation patterns graphically, we selected the functional finger ROI of M1 (see Materials and Methods) and split its voxels into 5 groups, depending on the contralateral finger for which they showed the highest activity (Fig. 3 B, left).
Moreover, in order to investigate the relationships between exons' splicing frequency and genes' splicing pattern, we have selected five main splicing patterns (cassette exons, mutually exclusively exons, retained intron, alternative acceptor site and alternative donor site) of alternatively spliced human and orthologous mouse genes from the ASTRA database (http://alterna.cbrc.jp/index.php).
To simplify the analysis of mechanisms underlying these patterns of transcriptional selectivity, we selected two genes, CA9 and PHD3, that manifest particularly strong hypoxia-inducible responses from low basal levels of expression in normoxic non-RCC (VHL competent) cells.
Through a pattern-matching algorithm, we selected the slip realisations from a large suite of randomised slip models that satisfied the given criteria on the spatial slip concentration (see the Appendix for further details).
As an exemplary cell, we selected patterned vertical alignment (PVA) mode.
Based on their localization, well-established neurobiological roles, and expression patterns in schizophrenic patients, we selected SYN2 and CPLX2 as candidate genes for conferring susceptibility to schizophrenia.
