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The models are built by defining the datasets and the actions (tasks) to perform on that data.
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Our method of defining the dataset for each lineage comparison (i.e. using those aligned repeats with identical RepeatMasker annotation in the four species in question) is subject to a sampling bias that depends on species divergence.
The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings/permutations.
These sequences define the ALL dataset.
Alternatively, a user can define the input dataset of homologous sequences for SIFT analysis (as done in the present study).
We define the targeted dataset X = { x1,..., x512} as consisting of 512 S PSC scores from step 1.
The genes significantly up- or down-regulated during germination in YPD and/or glucose define the core dataset used in the subsequent comparisons and consist of 1,203 genes.
One can also use prior information to define the training dataset for a supervised method which uses this information in order to guide the inference engine for the prediction of new interactions [ 13].
Combining the 454 and Sanger datasets resulted in 296 additional NAGNAG AS events being detected - of these, 66 had strong support for AS in terms of satisfying the criteria used to define the training dataset (≥ 2 reads for each variant, ≥ 10% of the reads for the minor variant).
For all other methods the following variations on parameters used to define the base dataset for endemic channel calculation were explored: (i) the number of historical years to include: five (current) or all available (long-term); and (ii) the inclusion of outbreak years in the base dataset, yes or no.
Integration of the results on detected pairwise conflicts (across all involved datasets) allows defining the conflicting regions separated by non-conflicting regions.
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