“highly significant”

Consistent with this structure, the STRING PPI enrichment analysis was highly significant (p < 1 × 10 −16 ), supporting the conclusion that these proteins form a biologically coherent interaction network rather than a random assembly ( Figure 9 ).

The network displayed an average clustering coefficient of 0.604 and a highly significant PPI enrichment ( p < 1.0 × 10−16), indicating substantial functional connectivity among the proteins.

However, while the difference between term and moderate to late preterm TREC was significant but not meaningful, 108 (70–170) vs 101 (64–164) median TREC ( p = 0.0017), the differences between term and extremely preterm, 49 (29–92), or very preterm, 88 (52–149), were both highly significant ( p = 1E −16 , p = 4E −6 , respectively) and meaningful.

However, larger differences were observed between organs that were highly significant ( P < 10 −16 for all four targets).

We found a strong and highly significant correlation between CpG O/E estimates in ortholog genes in S. mimosarum and S. dumicola (Pearson’s rho = 0.78 (0.77–0.79), p < 10 −16 ) ( Figure S9 ).

DAPs Are Involved in Wound Repair and Blood Coagulation Are Associated with Hydration Protein–protein interaction analyses of DAPs using STRING [ 19 ] resulted in a highly significant clustering (PPI enrichment value of p < 1.0 × 10 −16 ) consisting of 34 nodes and 126 edges ( Figure 2 ).

Furthermore, we validated rhythmicity using two alternative detection methods [RAIN (Thaben & Westermark, 2014 ) and ARSER (Yang & Su, 2010 )], which showed a highly significant overlap between the groups ( Appendix Fig S1G ; two‐sided Fisher's exact test, P < 10 −16 for every pairwise comparison).

The correlation between the distance of GO groups in the 0.001 cutoff co-evolution network (that is, their evolutionary distance) and their distance in the corresponding GO ontology network (that is, their functional distance) is highly significant: 0.38 for cellular component, 0.16 for biological process and 0.43 for molecular function (all three with P -values <10 -16 ; a similar trend is observed using the 0.01 cutoff network).

Overall, a test of independence between the risk score tertiles and the potentially AAMR CNV count classes shows a highly significant association ( P < 1 × 10 −16 ) for both Fisher's exact test and a χ 2 test.

However, the effects were clearly non-random, with highly significant enrichments of specific pathways and an overall protein–protein-interaction enrichment p -value of <1.0 × 10 −16 upon bioinformatics analysis at the STRING (search tool for the retrieval of interacting genes) webpage in Heidelberg.

The regression itself was highly significant ( R 2 = 0.93, P < 10 −16 ), and outliers were those points lying above or below a 99.9% confidence interval around the line of best fit.

Protein–protein interaction (PPI) analysis revealed a highly significant network connectivity among these proteins ( p < 1.0 × 10 −16 ).

The enrichment was highly significant (PPI enrichment p < 1.0e− 16 ), demonstrating that the observed interactions were unlikely to occur by chance.

We do detect a highly significant enrichment of marine-freshwater differentially expressed genes, as predicted ( P < 10 −16 ).

Results In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10 −16 ).

The network comprised 65 nodes and 400 edges, with a highly significant PPI enrichment p-value of < 1.0 × 10−16(Fig. 1 B).

Acquisition and analysis of intersection targets of ROSA and CHIKV- The PPI analysis of the CHIKV disease target network revealed a total of 502 nodes and 1,386 edges, with a highly significant p-value of < 1.0e-16 ( Figure 2A ).

This test yielded highly significant results for both similarity types (Functional and Taxonomic) across all three runs (Taxonomic: H-statistic 80.64–107.68.64.68, p < 1e-16; Functional: H-statistic = 380.5–457.7, p < 1e-80), indicating that at least one category distribution differed from all the others.

The overlap between our set of 2,407 genes and this set of 1,092 genes was highly significant (p<1x10 16 , Fisher’s exact test, S2B Fig ) and, since RNA-seq is typically more sensitive than microarray analysis [ 31 ], we proceeded with the larger gene set.

Results Strong, negative association between elevation & lung cancer incidence Performing best subset regression for each cancer, we found a highly significant, strong negative association between elevation and lung cancer incidence with a standardized coefficient ( β z ) of −0.35 99% CI [−0.46, −0.25] ( p < 10 −16 , one-tailed t -test) ( Table 2 ).

The trend is weaker in lincRNAs compared with protein-coding genes (rho = 0.2 and 0.28, respectively) but both correlations are highly significant (from Spearman: P < 10 − 16 ).

The interaction between fingertip ROI and stimulation condition was highly significant ( F 16,80 = 71.7, P < 10 −16 , Greenhouse‐Geisser correction: F 1,5 = 71.7, P < 10 −3 ).

The correlation between RNA-seq fold changes computed on the data from day 2 versus that from day 4 was highly significant (r = 0.50, P<10 −16 , Figure S2A ).

However, when considering the overall number of associations per source and the number of highly significant associations (-log 10 (p) ≥ 16), clear differences emerge.

Across all 10‐kb windows in the genome, there was a weak but highly significant correlation in the distribution of Tajima's D values in the two clusters (Figure 4 b; Spearman's ρ = 0.25; p < 10 −16 ).