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To validate the effectiveness of the method in event-related paradigms, three archived datasets were analyzed by two manual raters, the fully-automated method (Autonomate), and three alternative software packages.
In this retrospective study, datasets were analyzed by the same performer before surgery and re-analyzed 12 and 24 months later to determine proficiency and results were compared between the analyses.
BC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays.
Both datasets were analyzed by repeated measures ANOVA with subsequent Dunnet's multiple comparisons test against WT pKi in GraphPad Prism.
Gene expression datasets were analyzed by Cluster 3.0 which was originally developed by Michael Eisen, with parameters as hierarchical clustering, uncentered correlation, complete linkage (http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm ctv), and visualized in software Java TreeView [81].
Multiple datasets were analyzed by one-way ANOVA.
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The stationarity of the datasets was analyzed by using unit root tests.
A previous review of meta-analytic results derived across these methods [5] found modest consistency of results for schizophrenia and an absence of replication for bipolar, and discrepant results were attributed to differences in the datasets being analyzed by the two methods.
These multiple imputed datasets are analyzed by using standard procedures for complete data.
Each of these datasets was analyzed by using the Bayesian skyride method (29 ) in BEAST.
After MI, each of the simulated complete datasets was analyzed by standard methods, and the results combined through the process of multiple imputation inference to obtain parameter estimates (AORs) and CIs that incorporate missing-data uncertainty (SAS PROC MIANALYZE) [ 36].
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