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Many biomedical and healthcare studies may be significantly impacted by using large, heterogeneous and incongruent datasets; however there are significant technical, social, regulatory, and institutional barriers that need to be overcome to ensure the power of Big Data overcomes these detrimental factors.
In this vein, we contribute a set of best practices, which focusses on deriving "boundary-corrected" wavelet and scaling coefficients from time series data, overcoming the boundary condition issues and providing hydrological and water resources modellers with a justified and coherent strategy for developing wavelet-based forecasting models that may be used for real-world forecasting problems.
Our study with the use of individual data overcomes the cross-level bias that characterizes aggregate analysis [36], [37].
Other published data overcoming these limitations were not found.
We successfully used BEAST to infer divergence ages from the UCE data, overcoming computational limitations through data filtering.
Challenges common to these approaches include inferring causation from spatial data, overcoming multicollinearity, overcoming the complexity of species interaction networks, spatial and temporal variation in biotic interactions, and data paucity.
In the present study, we have developed an updated computational approach for the prediction and annotation of ES proteins using NGS transcriptomic data overcoming the limitations of the earlier EST2Secretome pipeline.
EHR-based studies sample patients based on sufficiency requirements with the aim of selecting only those records containing sufficient data to overcome the data missingness problem.
After applying an appropriate re-sampling procedure on the observed data to overcome the problem of data heterogeneity among the different loci, we first estimated several statistics describing genetic diversity in the region under study (Table 1 and Figure 1).
Corrections were applied to the ND data to overcome the surface effect and data reduction was performed to coherently interpret the results, including the determination of the centre-of-mass of the diffracting volume.
Analyses of cost data must overcome several statistical problems, including data skewness and heteroscedasticity (11).
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Justyna Jupowicz-Kozak
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