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Our method utilizes computational predictions of protein-ligand binding across the human proteome.
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The proposed method utilizes two computational models; the first model represents cell growth patterns whereas the second model determines optimal initial cell seeding positions for adherent cell expansions.
CAMM also utilizes computational linguistics methods for comparison of clinical annotations from multiple datasets.
This study utilizes computational topology optimization methods for the systematic design of optimal multifunctional silicon anode structures for lithium-ion batteries.
The computational method utilizes a newly derived interface boundary conditions as well as an adaptive filtering technique to stabilize the computations.
The computational method utilizes the Bayes theory (4) to calculate the posterior density of unknown fluxes and parameters f u| y,v) defined as: f (u | y, v ) = f (y, v | u ).
To test this prediction, we first utilized computational methods to predict conserved fRNA secondary structures within multiple species alignments of Saccharomyces sensu strico genomes.
Systematic screens for sRNAs have focused mostly on intergenic regions and utilized computational methods [ 5– 10], shotgun cloning strategies [ 11, 12] and high-density oligonucleotide probe arrays [ 13– 15].
We therefore utilized computational methods to test the hypothesis that some of the genes within this large up-regulated cluster are co-ordinately regulated by common transcriptional mechanisms.
As these mutations were not reported in earlier studies, we further characterized the effect of these mutations on the protein function and stability utilizing computational methods such as SIFT, Polyphen, and MD simulations.
Namely, since the number of pooled reads is very large, our method utilizes the position of the corresponding anchor reads to improve computational efficiency by filtering out false sequences.
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