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As seen in "Linear SVR" section, the goal in ε-SV regression is to find a function with the most ε-deviation from the obtained targets for all the training data, and at the same time as flat as possible.
The goal in ε-SV regression is to find a function (fleft( x right)) with the most ε-deviation from the obtained targets (y^{l}) for all the training data, and at the same time as flat as possible (Vapnik 1995).
In order to test these propositions we have first obtained targets from TargetScan [ 21] for the families of 101 loosely defined down-regulated and 97 up-regulated miRNAs.
From the Additional Data of the paper by Gerstein et al. [ 21], I obtained targets detected by ENCODE, including both proximal and distal sites.
In above ε-SV regression, the goal is to find a function f(x) which has the most ε deviation from the actually obtained targets y i for all the training data and as flat as possible.
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The obtained target molecules, however, showed only negligible affinity to the GAT-1 and GAT-3 transport proteins.
Based on the positive results that we obtained targeting Nfix in dystrophic mice, we decided to further investigate the link between Nfix expression and the progression of the pathology.
After transformation into categorical variables (obtained target = 1; outside the target = 0), analyzes were completed by a concordance study (Kappa).
Using an E-value threshold (1E-50), we obtained target mapping between BindingDB sequences and drug target sequences from DrugBank.
These results are approximately close to the obtained target displacements from FEMA-356; however, they are a little lower.
The heat transfer coefficient was determined via numerical simulation, by finding a value that satisfied the experimentally obtained target condition.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com