Sentence examples for model we classified from inspiring English sources

In our model we classified 1 kb genomic bins into 3 categories: enhancer positive (Enh), promoter-like (PrL) and unknown.

In the original model we classified sex workers, clients, men who have sex with men (MSM) and injecting drug users (IDUs) as 'most-at-risk-populations' (MARPs).

To summarize the effect of including additional explanatory variables into the model, we classified for each model the hospitals into seven categories, ranging from strong, then moderate, to weak evidence of finding themselves above or below the inconclusive zone.

In the regression model, we classified cases and controls into the following age categories, ≤50 years, 51 60 years, 61 70 years, 71 80 years or ≥81 years to adjust for potential confounding by age.

For each cell (discrete model) or discretized volume (microscopic model) we classified the electrical activity observed during the simulations as NA (no activity), A (a single AP, no reentry), S (sustained reentry, multiple APs until the end of the simulation), or NS (nonsustained reentry, multiple APs, but activity stops, dies out, before the end of the simulation).

In this affiliated business transaction model, we classify Internet sites into two groups: a cooperative sites group, and a non-cooperative sites group.

In our model we classify individuals into three age groups: children (aged from 0 to 15 years), younger adults (aged from 16 to 30 years), and older adults (aged 31 years or older).

After adjusting the three models we classified our dataset and obtained three initial catalogues of putative synaptic genes (Fig.  3).

In both models, we classified male mating behaviour per season as floater or territorial and accounted for the fixed effects of overall intercept and age (linear and quadratic) and additionally fit year as a random effect.

For a more qualitative comparison of the two models, we classified individual elements of the sensitivity matrix as either "activating" (S = 1), "inhibiting" (S = -1) or "neutral" (S = 0), in which threshold sensitivities of ±0.003 (determined by visual inspection of the histograms of individual sensitivities from the two models) were used to reassign individual values.

In the models, we classified a patient's case mix as a fixed effect (that is, the patient's age, sex, body mass index, weight of thyroid specimen, and complexity of surgical case), the surgeon as both random and fixed effects (that is, the length of experience and number of surgical procedures done by the surgeon on the same day), and the hospital as a random effect.