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Besides the induction of correlated observations within the same inspector, the mixed model approach also enables one to specify the covariance structure of the inspection data.

The model approach also has the advantage that herds where only a few animals were sampled are adjusted for the general seroprevalence avoiding overestimation.

The mixed model approach, also known as multilevel modelling or hierarchical linear modelling will be used to examine the intervention effect (group and time interaction).

Investigation of MAPKs in G. max in another study using the HMM (hidden Markov model) approach also showed 35 MAPKs; however, four of them are indeed 'MAPK-likes' genes, making the actual MAPK number 31.

The mixed-effects model approach (also called random coefficient model or multilevel model) will be used to account for correlated data introduced by the repeated measures study design, due to its versatility in the modelling of the time factor and in allowing varying numbers of measurements per individual.

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The ontological modelling approach also enables a range of operation reasoning which can provide assistances for advanced tasks such as simultaneous, chained and service orchestration operations.

Furthermore, the availability of an alternative reduced order modeling approach also facilitates the handling of very large and complex signaling networks.

Our metabolic modeling approach also predicted that the KD of the next enzyme of the non-oxidative PPP, ribulose phosphate-3-epimerase (Cre12.g511900) results in a ρ > 1.

Because our aim was an overall synthesis using a large ensemble of species, rather than a focus upon individual species, the uncertainties arising with respect to individual species as a result of differences in future range predictions made by models fitted using different modelling approaches also are of minimal importance.

Intriguingly, one of our mathematical modeling approaches also predicted that Pmr1p is involved in longevity (see below).

Both statistical modeling approaches also identified an association between the number of farms with breeding sows, either expressed in absolute terms (logistic regression) or as density (BRT).

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