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Given these premises, it is not surprising that different GRNs are available in the literature for the same organism, and a choice had to be made to determine the datasets better suited for our analysis.
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To determine the dataset's utility for suggesting genes associated with different experimental factors, we identified genes that were differentially expressed between males and females (gender-selective genes) using EPIG, a profile-based analysis method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes [ 13].
Accordingly, the assumptions and the process by which the data in the literature was used to determine the accelerated dataset are described below for each technology separately.
Overlapping SNPs in both datasets are used to determine the comprehensiveness of dataset.
GENN with 1,000 population size per deme and 10 demes was employed for each genomic dataset with different threshold in order to determine the best filtered dataset prior to modelling.
We used simulations to determine the effects of dataset size, branch length heterogeneity, branch depth, and analytical framework on branch length estimation across a range of branch lengths.
As a first step we will compare the distribution of the predictors of the PIAMA Risk Score and the asthma outcome in the development (PIAMA) and validation (Generation R) data to determine whether the datasets are comparable.
The same as in the first experiment, we must firstly determine the requirements for the dataset capturing.
To gather meaningful performance data, we must firstly determine the requirements for the dataset capturing.
Principal component analysis (PCA) was performed to determine the structure of the dataset of almost 6 million data points generated by the experiment.
Evaluating GLEAN runs with different combinations of input sets allowed us to determine the optimum selection of datasets.
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CEO of Professional Science Editing for Scientists @ prosciediting.com