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A new empirical likelihood approach is developed to analyze data from two-stage sampling designs, in which a primary sample of rough or proxy measures for the variables of interest and a validation subsample of exact information are available.
Missing data is a problem that is ubiquitous to all clinical studies and a source of multiple problems from an analytic point of view (reduced statistical power, increased the type I error, bias) Statistical approaches have been developed to analyze data collected from trials with missing data.
Many methods have been developed to analyze data from case control studies.
It was developed to analyze data from LLMDA arrays [ 19], but could in principle be applied to other whole-genome arrays such as the ViroChip.
The method was originally developed to analyze data from the Valencian Sentinel Network (VSN) for influenza surveillance, a system which collects information on influenza-like illness (ILI) in the Comunitat Valenciana, one of the 17 autonomous regions in Spain.
This is also the case in post-marketing drug safety surveillance, where the discovery process typically relies on large samples; computational signal detection algorithms have in this context been developed to analyze data with the purpose of detecting signals of potential ADEs [ 17].
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Other programs, developed to analyze mammalian CGH data, analyze data by smoothing probe intensities, followed by breakpoint identification by scanning for areas of high contrast between smoothed data values of neighboring probes [ 12- 18] 18].
To this end, a software program (Ingenuity Pathway Analysis), specifically developed to analyze large data sets such as microarray data for biological functionalities, gene networks, and physiological pathways [ 28], was used to assign biological functionalities and molecular interactions in chicken embryo lung cells after 1 to 7 days in response to ILTV infection.
To the best of our knowledge this is the first freely available software developed to analyze variant data from multiple individuals that rapidly assimilates and filters large data sets based on pattern of inheritance.
Even though various computational approaches have been developed to analyze these data, it is still a laborious task involving prudent integration of many heterogeneous and frequently updated data sources, creating a barrier for interested scientists to accomplish their own analysis.
Several soft and hard modeling algorithms have been developed to analyze bilinear data obtained from chemical systems.
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developed to integrate data
developed to obtain data
developed to focus data
developed to query data
developed to exchange data
developed to acquire data
developed to permit data
developed to enable data
developed to perform data
developed to combine data
developed to measure data
developed to provide data
developed to gather data
developed to support data
developed to process data
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