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Computationally, many tools have been tailored to take advantage of the high-resolution of scRNA-seq data and deconvolute noise [56].
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Since the alignment process is computationally intensive, many alignment tools are designed as parallel applications, typically targeting multicore platforms.
The presence of a motif in the up-stream region of a gene is determined computationally and many of them may not be functional binding sites of transcription factors.
The approach is personalized, based on the contributory laboratory values affecting the plasticity network; and is computationally intensive, since many physiological data points are analyzed, interdependent network-component status is assessed, and many interventions are prioritized to determine the therapeutic program.
This is computationally intensive, requiring many calculations in real-time.
Furthermore, it is more computationally effective than many alternative algorithms, making the learning phase faster.
However, kinetic simulations are still too computationally intensive for many large scale 3D simulations.
Many computationally intensive algorithms are often represented as n-dimensional (n-D) nested loop algorithms.
Additionally, evaluation of objectives and constraints is computationally expensive in many applications.
Discrete transforms are of primary importance and fundamental kernels in many computationally intensive scientific applications.
However, they are computationally expensive as many multiply accumulate (MAC) operations are necessary for each pixel of the resulting image.
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