Sentence examples for complexity to predict from inspiring English sources

ENM fate models have to be designed and evaluated which are capable of incorporating the environmental complexity to predict realistic environmental concentrations of ENMs.

We develop a suite of models with varying complexity to predict elk movement behavior during the winter on the Northern Range of Yellowstone National Park (YNP).

8 This complexity to predict phenotypic cancer diversity by genotyping explains the current limitation in development of both rousts biomarkers and molecularly targeting agents toward innovative personalized cancer management.

Sorafenib is a multi-kinase inhibitor and it is likely that limiting our signatures to thirty features each did not allow enough genomic complexity to predict response to this drug.

An added complexity to predicting whether a compound binds to PXR is the recent discovery that some molecules can also bind outside the LBD on the PXR surface and act as allosteric antagonists (Ekins et al. 2007, 2008a).

Zimmermann et al. exploited historical bug data and code complexity features to predict vulnerable components[36].

The complexity and inability to predict the canal morphology brought him to name the whole system as the radicular canal system (Kanalsystem).

Prediction models for episodes have so far considered covariates correlated with symptom severity, treatment complexity, or remission to predict long-term outcomes (Busch et al. 2012).

This is particularly true because one alternative explanation is that biological differences between species make AR in principle, based on complexity science, unable to predict responses in humans.

The presence of multiple disease states in critically ill patients with AKI adds another layer of complexity when attempting to predict how AKI changes drug metabolism and nonrenal clearance.

Although numerous risk stratification tools have been developed and validated for individual disease groups [ 8– 11] there is a lack of generic measures that can be used across different conditions, ages and complexity of disease to predict individuals at high risk of readmission to acute care.