The objective is to optimally locate sensors in the structure such that the resulting measured data are most informative for estimating the parameters of a family of mathematical model classes used for structural modeling.
It has been suggested that annotation-based metrics are most informative as they estimate the number of real genes that can be recovered from the assembly [ 50].
As explained above, the most informative numbers for estimating the radius of the passive channels are the ratios of the nuclear entry rate constants k i to that of our internal reference, thioredoxin kref.
In the majority of occupational cohort studies, which we regard as most informative, specific LHM risk estimates were consistent with the null value, with few exceptions, where the excesses were generally small (i.e., RR < 1.5) and statistically imprecise.
For example, as a measure of physical function, our index suggests that the number of step-ups completed by a subject in 10 seconds was the most informative variable, but concordance estimates of the index decreased only slightly when walking speed or grip strength was substituted for this variable (Table 2).
However, limited number of cases prevented us from obtaining informative estimates for most of specific childhood cancers.
This research suggests that the most informative measures for doctors may be estimates of the post-test probability of disease (predictive value), which can be presented as a range corresponding to different pre-test probabilities.
In addition, the contribution of Isolation by Distance (IBD) was estimated using the most informative gene data set (COI) in GenAlEx 6.1 [ 51] with 999 permutations using the Mantel test in GenAlEx 6.1, initially for the group as a whole and subsequently for each clade individually, excluding the Zambian and Malawian clades that originated from one site each, preventing analysis.
In both the network topology and parameter inference challenges, participants were asked to develop and/or apply optimization methods, including the selection of the most informative in silico experiments, to accurately estimate parameters and predict outcomes of perturbations from a model of a gene regulatory network.
Focussing on the LQTS model because of the much larger training set available, we investigated which predictors are the most informative, and assessed the biological plausibility of estimated effect sizes.
