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Both computational scalability and theoretical guarantee of the traditional models are compromised by such high dimensionality [13].
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Software engineering approaches employed in OMS3 are highlighted including a non-invasive lightweight framework design supporting component-based model development, use of implicit parallelism in system design, use of domain specific language design patterns, and cloud-based support for computational scalability.
This tool provides both the prototyping flexibility to investigate more complex physics and non-linear feedbacks as well as the computational scalability to tackle three dimensional scenarios.
The most pressing one is the computational scalability of persistent homology, which currently prevents large-scale applications.
On the other hand, we have turned to Hadoop-based tools to improve our computational scalability.
In comparison to deterministic global optimization algorithms, metaheuristic methods have better computational scalability.
Additionally, much larger size of mammalian genomes will undoubtedly be a challenge to computational scalability of machine learning methods.
While the algorithm can be generalized to align sequences containing any number of SVs, its most practical (due to computational scalability) application is to align sequence with one SV, which are also the most common.
Our discussion considers computational efficiency, scalability, and some implementation issues of the solution methods.
The results indicate that the framework could have a wide range of bioinformatics applications while maintaining good computational efficiency, scalability, and ease of maintenance.
Both computational and analytical techniques are adopted.
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