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Using the fitted models, we inferred the period, amplitude, and phase of all cycling genes in the C. reinhardtii genome.
Then we present and discuss the training/validation errors from the 11-gene GRN models we inferred from a yeast data set.
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Measured against the Jeffreys' [22] scale, this indicates very strong support for our posterior distributions and the demographic models we infer from them.
Therefore, we apply KInfer to the network model we inferred with the time-lagged correlation based inference procedure described in the previous section.
We submit that any additional papers identified did not have a material influence on the model we inferred from our literature synthesis.
For populations that failed to reject variants of a two-phase growth model, we inferred growth parameters with greater resolution using approximate Bayesian computation (ABC) [47].
Under the framework of linear model, we inferred the differential expression based on the following collected patients' characteristics: EAC stage (late n = 5 vs. early n = 5), USC stage (late n = 5 vs. early n = 5), EAC prognosis (good n = 6 vs. poor n = 4), and USC prognosis (good n = 6 vs. poor n = 4).
To test our model, we inferred protein localization in 21 genomes.
To illustrate the interest of multi-state modelling, we inferred the capacity of E. coli to use lactose and maltose for various concentrations of glucose.
Using the relaxed evolutionary clock model, we inferred a rate of substitution per site per year of 2.5 × 10−4 for ORF Ash and 1.7 × 10−4 for ORF Bsh.
For our model, we inferred that the suicide risk would remain unchanged in the best case (9, 13) and double in the worst case scenario (14, 15).
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