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Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset.
Among these patterns, the two most significant patterns were profiles No. 9 and No.16 according to ascending p-values.
In contrast, the next two most significant patterns of gene expression reveal coordinated changes in the expression of key genes involved in metabolism.
Among 22 expression profiles, the two most significant patterns (profiles No. 2 and No. 76) according to their P-values had the largest number of differentially expressed genes, including 239 and 245 genes, respectively.
Strikingly, the most significant patterns, accounting for the most variability among patients, were levels of circulating proinflammatory cytokines (PC 1), a biologically sensible result given extensive documentation of the role of soluble inflammatory mediators systemically and in affected tissue in childhood arthritis.
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This study shows that in MC3T3 preosteoblasts focal adhesion kinase (FAK) Y397 phosphorylation, cell spreading, and osteogenic differentiation can be controlled by RGD nanopatterning, with the distribution of islands throughout the hydrogel (i.e. how closely spaced the islands are) being the most significant pattern parameter.
The most significant pattern that arises from our analysis is that these location-specific functional BSs are concentrated in a distinct region of about 300 bp that includes the TSS; the abundance of such BSs further upstream is significantly lower.
The most intense perturbation outranks the topologically most significant pattern instances in the other two networks.
In the networks, the topologically most significant pattern instances consist preferably of genes that provide basic functions for the organism.
The second most significant pattern identified by SVD corresponds to broad upregulation of gene expression midway through embryogenesis and is strikingly similar to the coordinate induction of glycolytic genes that defines the EmbMT (eigengene pattern 2, Figure 1A).
To select the most significant DDI patterns occurring within the PPIs in each group, we assess the significance of each DDI pattern in a group by computing its χ-value using the formula (1) where N is the total number of PPIs in the network, A is the number of PPIs in the group that contains the DDI pattern and B is the number of PPIs outside the group that contain the DDI pattern.
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