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We develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner.
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Allowing the modeler to draw pre-existing modules from a repository or extract them from previously developed models and enables the modeler to create new models quickly for hypothesis testing, a key to Physiome development (Bassingthwaighte et al., 2009).
The present study will familiarize the reader with various optimization techniques of system modeling and enable them to compare these models on the basis of their cost functions.
This is a natural fit for the flexible document data model and enables easy extension to new log formats.
Our approach relies on an abstract interface and adapter model and enables the generation of executable adapter code.
This analysis provides quantitative insights into the uncertainties that underlie hydraulic fracturing models and enables the identification of potentially influencing factors to reduce performance uncertainties.
Finally image-based reconstruction automatically provides photo alignment with point cloud models and enables image-based renderings which can remarkably impact automated performance monitoring and as-built visualizations.
Their approach models textures by using the MRF (Markov random field) model and enables missing texture reconstruction by copying pixels of a target image itself, that is, nonparametric sampling in synthesis.
That technique is advantageous since it facilitates representing the aquifers by dynamic models and enables authorities to assess the process through data acquisition and updating the data, planning, adjustment, comparison and interpretation.
The method relies on a clustering algorithm based on a Gaussian mixture model and enables determination of the likely number of clusters considered as the "true" QTLs underlying the QTLs observed in a given region.
Objectivity is recognizing, accepting and responding to "what is" without projecting our mental models and enables us to respond thoughtfully, appropriately and effectively.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com