Sentence examples for data overcoming from inspiring English sources
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.
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.
Recently, with the rise of smartphones, other methods of collecting data overcoming those drawbacks have been designed: projects such as Reality Mining [5], OtaSizzle [6], or Sensible DTU [7] consist in distributing smartphones to individuals who volunteered for the study.
Our data overcomes the limitations associated with other in vitro models used to assess structural cardiotoxicity.
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.
Our study with the use of individual data overcomes the cross-level bias that characterizes aggregate analysis [36], [37].
Integrating diverse feature sets of genes and diseases obtained through a wealth of publicly available data overcomes extreme sparsity in the gene disease associations data.
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.
