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However, most studies that used a distributed modeling approach relied on one or a few sites for model testing.
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Along with finding statistically relevant CNA co-occurrences, our algorithm points towards a generally low specificity for co-occurrence of regional imbalances in CNA rich samples, which may have negative impact on pathway modeling approaches relying on frequent CNA events.
The static model approach relies on the observations made using a sample query over the database.
As discussed above, the static model approach relies on the observation data generated using a sample global query (Q_G).
However, the model selection approach relies on forming a set of candidate models that are representative of the biological processes under investigation [ 70].
Like most statistical models, our approach relies on some simplifying assumptions.
The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode.
Both model-assisted and model-based approaches rely more heavily on models and auxiliary variables to produce estimates than traditional design-based approaches and therefore may produce more precise estimates for small areas (McRoberts et al. 2013).
This approach relying on modeling enzymatic hydrolysis process at molecular and enzymatic levels has been successful in describing starch hydrolysis [ 38- 42].
The proposed approach relies on the modeling of faults as additional scheduling parameters of the LPV model for the controlled system and it uses a triple loop architecture.
Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.
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