Sentence examples for success of the computational from inspiring English sources

The success of the computational procedure presented here stresses the importance of not being limited to local sequence characteristics for functional prediction.

Considerable success of the computational modeling approach used in the present work points to the possibility of precise computational predictions of cell-level effects of such therapeutic interventions, which could facilitate their rational development.

Accurate representation of the effects of water as a solute is critical for the success of all of the computational methods discussed (MD, docking, and ΔG calculations).

Because the choice of regularization determines the solution to equation (1), the success of a computational purification method depends on the suitability of the regularizations that it applies.

To evaluate the success of our computational algorithm for identifying direct targets of the a1- α2 repressor complex, we assayed binding by the complex to the identified promoters using chromatin-immunoprecipitation (ChIP) assays with polyclonal antibody directed against the α2 protein.

The success of contemporary computational neuroscience depends on large amounts of heterogeneous data, powerful computational resources and dynamic web-services [7].

Finally, the success of any computational approach for constructing genetic interaction networks is likely to be driven by parallel improvements in the experimental technologies, such as enabling measurement of phenotypic effects in response to the mutation of more than two genes in combination.

The success of computational TFBS discovery methods critically depends on the a priori knowledge about which genes are co-regulated or functionally-related and therefore more likely to share common TFBS.

The success of computational methods in systems biology and systems engineering relies on the availability of mathematical models which represent the biological system adequately The process of model development, model analysis and model invalidation is, however, often limited by the availability of suitable experimental data leading to impaired significances of the models.

The model reduction is extended to the sensitivity analysis and is a key element for the success of computational design of deformation processes.

While decades of studies on protein protein interaction thermodynamics provide a strong underpinning to the successful computational design of novel protein folds and de novo proteins with enzymatic activity, the corresponding paucity of data on metal protein interaction thermodynamics limits the success of computational metalloprotein design efforts.