We close this section by mentioning the highlights talk given by Mark Ragan (The University of Queensland), who reported an analysis of human transcriptomic data to elucidate the effect that alternative splicing has on the protein-protein interaction network, via inclusion or omission of certain interaction domains [ 36].
This omission of multipole interaction terms is one of the key limitations in the dipole-dipole interaction model, which leads to a significant underestimation of plasmon coupling at extremely small gaps.
Results from many association studies have not been replicated, possibly due in part to the omission of incorporating interactions among disease associated loci in the analysis where allele frequencies for interacting genes or environmental exposures may have differed in the investigated populations [ 1].
Instead, we find that the crucial factor in discriminating among topological pathways is the heterogeneity of native contact energies: The order in which native contacts accumulate is profoundly insensitive to omission of nonnative interactions, provided that native contact heterogeneity is retained.
Observed HR and GS values display linear trends when compared to values predicted by the top-ranking models (Fig. 2a b), indicating that specification of main effects only did not result in poor fit due to omission of any major interaction terms.
The omission of the spousal interactions section from WYMPI after baseline administration will mean that this cannot be examined in relation to change, but it will help categorise our sample at baseline.
We show that the omission of one or more interaction effects can result in biased estimates and misleading inference.
Dashed lines are two standard errors above and below the fitted values To test the sensitivity of this result to our analytical methods we tested for associations between co-infection death, age, and sex using other settings; in all instances we found omission of the age:sex interaction to increase AIC by at least 25 (Additional file 1 : Table S1).
To test the sensitivity of this result to our analytical methods we tested for associations between co-infection death, age, and sex using other settings; in all instances we found omission of the age:sex interaction to increase AIC by at least 25 (Additional file 1: Table S1).
A further limitation of the study was the omission of the potential implications of interactions between the level of susceptibility, test properties and quit rates that may impact on the cost-effectiveness findings, introducing further uncertainty.
