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The analysis of the results is difficult through statistical techniques, and needs restrictive assumptions for the problem to be analytically formulated, or a prohibitively large computational endeavor.
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It is hoped that these relations will prove useful in engineering or computational endeavors where the speed and/or ease of their use may be advantageous.
This is not a new question; it comes out of the overall recent statistical and computational endeavors that focus on feature selection from massive and highly dimensional genomic data.
In this endeavor, computational tools have proven useful in accelerating drug discovery.
The marker reported in our database are ready to use for mapping as well as also for variety identification and product traceability, paving the pathway of best use of genomics and computational tool in endeavor of tomato improvement and variety management at global level.
The paradigm of Aβ amyloid structure elucidation provides an example of an innovative experimental design and endeavor, echoing the computational testing of possible molecular associations, all reflected in the current Ma-Nussinov-Tycko model of the Aβ amyloid.
Success of these endeavors depends on computational accuracy, especially in regions of functional importance or surface regions that serve as ligand binding sites on a protein in the intact, native state.
The increasing amount of heterogeneous data Big Data needed for that endeavor typically requires large computational and storage resources.
This endeavor relies heavily on computational modeling, and while many approaches have been described, most were field tested prior to systematic analysis of TF sequence specificities by PBMs and MITOMI, before the importance of the intrinsic sequence preferences of nucleosomes was widely appreciated and before the use of tiling arrays and ChIP-seq became prevalent.
This endeavor, however, has been facilitated by computational genomics approaches that leverage deep sequencing technologies.
However, the computational analysis that we put forward facilitates this endeavor by pointing those protein sets with higher chances of participating in functional tetrameric complexes.
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