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For the latter high-level quantum chemical calculations would be required, capable of dealing with bond-formation and –breaking as well as of treating electrons explicitly, in contrast to force field-based methods which handle atoms.
Recent studies [ 15, 16] have established the power of machine learning methods, which handle PPI extraction as a classification problem.
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This has inspired us to investigate a characterization method which handles non-star-shaped contours.
Energy and Buffer Aware Application Mapping (EBAM) is a recently proposed method, which handles the application mapping issue as a joint optimization problem for minimizing the energy consumption and buffer utilization simultaneously.
An overall P-value for each model term is calculated by the 50 50 MANOVA method, which handles collinear responses.
We propose SNP-based parametric robust analysis of gene-set enrichment (SNP-PRAGE) method which handles correlation adequately among association measures of SNPs, and minimizes computing effort by the parametric assumption.
This method uses rescue manipulators, which handle the rescue tool we designed to drive failed actuators externally.
Therefore, the transcript quantification from RNA-Seq data was performed using the Expectation-Maximization method (RSEM), which handles read mapping uncertainty with a statistical model by estimating maximum likelihood expression levels [ 58, 65].
and, which handle dynamic memory.
Genetic algorithms have been presented as a family of methods which can handle most of engineering problems.
More suitable methods, which can handle the aforementioned complications, can be based on data augmentation and Bayesian inference and implemented, for example, by using the OpenBUGS software [12, 13].
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