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However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability.
To the best of our knowledge, analysis of retraction force curves according to these models is commonly done via a manual, ad hoc and time-consuming specification of the various intervals in spatial separation between AFM tip and sample.
The performance of prognostic models is commonly evaluated by discrimination and calibration.
Balance between exposed and unexposed subjects in propensity models is commonly assessed by using the standardized difference (16).
Study of colorectal cancers using age-period cohort models is commonly used in order to better understand the observed trends and aetiological factors connected with them [ 5, 6].
Increased CD36 expression in human and rodent models is commonly related to insulin resistance and type 2 diabetes, including high-fat (HF) feeding and obesity [ 16– 18] and is also observed in the physiological aging process [ 19– 219.
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Four models are commonly used in the United States, with Food and Drug Administration approval.
Predictive models are commonly assessed using c-statistics.
Transgenic mouse models are commonly used in AD research.
Shallow-water type models are commonly used in tsunami simulations.
Xenograft models are commonly used in oncology drug development.
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