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The following methods are especially useful.
Relaxation methods are especially well suited for studying fast reactions comprising an enzymatic reaction cycle.
Such methods are especially useful in spatial data domains such as medical scan data and geophysical data.
At a very granular level, experimental methods are especially adept at assessing causality; supervised machine learning excels at prediction where non-supervised machine learning can decompose non-numerical stimuli into tags or attributes for further analysis.
Probability density function (PDF) methods are especially suited for turbulent combustion calculations, since the averaging of the reaction source term in the governing equation poses no closure problem.
We focuSoft how the combination of theory and experiment has been applied and can be further developed to drive our understanding of how these methods probe relevant chemistry, structure, and dynamics in soft materials.
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Expanding the number of transcription factors analyzed by ChIP-based methods is especially informative in dissecting system level biological processes.
These methods were especially proposed for analyzing the observed data where a large proportion of genes contain no Alus in their intron sequences.
The combination of XRPD data with quantum-mechanical methods is especially attractive, since they complement each other's strengths and weaknesses very well (Fig. 1 ▶).
The advantages of the new method are especially pronounced when adjacent overset subdomains are discretized with different spatial resolutions.
The advantages of our method are especially dramatic in high-dimensional parameter spaces.
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