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In this example we generate the data in a similar manner as in [12].
In this example we generate an XML-based representation of the experiment as a whole (multiple tasks), with a limited granularity for the description of each task.
For example, we generate highly specific variant resources from experimental crosses based on markers that segregate as expected (see the section Materials and Methods).
In the first example we generate a large model for a genetic regulatory network that can be used to produce ground truth data for a microarray simulation [ 25].
For example, we generate 100 independent training and test sets for the BreastIBC data, where each sample has a 50%% likelihood to be selected in the training and test set.
As the first example, we generate an unstable network model [see Additional file 4] by requiring at least one positive eigenvalue for the system matrix A. The structure of the generated network is depicted in Figure 5.
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As a practical example, we generated knock-in CHO cells producing an scFv-Fc antibody using the CRIS-PITCh system mediated by microhomology sequences for targeting.
As an example, we generated the subnetworks of these two genes in both datasets depicted in Fig. 2. Nodes which are closer to the MR demonstrate a higher MI degree.
For example, we generated $1.9 billion in display revenue on our owned and operated sites in the last 12 months (ending September).
As one example, we generated and characterized lines of mice lacking the major physiological receptors for angiotensin II in the mouse.
For example, we generated an inducible VA13 cell line to test two shRNAs against the DNA repair protein XPG.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com