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.
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 highly specific variant resources from experimental crosses based on markers that segregate as expected (see the section Materials and Methods).
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.
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.
For example, we generate an ensemble of predictions of C(t+ k), denoted { C ^ (r ) (t + k ), r = 1, 2, …, M }, where r represents a single realization and M is an upper-bound on the number of realizations, and summarize over the ensemble to obtain more accurate census forecasts.
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.
