Sentence examples for recover a network from inspiring English sources
Our inference method is based on a linear dynamical system, so it follows that it is easier for it to recover a network from data created with a linear model.
Notice that given a sbTSTC phylogenetic network, it makes sense to apply to it the inverse of any of the reductions introduced, simply following the procedure to recover a network from its reduction.
The basic idea of our procedure is to decompose the problem of recovering a network involving p genes into p different subproblems, where each of these subproblems consists in identifying the regulators of one of the genes of the network.
In a recent evaluation, Werhli et al. [ 39] showed BNs to have good performance at recovering a network based on the Raf signalling pathway identified in [ 8] from synthetic data generated in a number of ways, and documented the differences in performance for learning from observational and interventional datasets.
While we can thus recover an effective network, the knowledge of A ˜ does not uniquely determine A, B, C, or D, or in fact even the number M of unobserved variables.
To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data.
For example, what is the gain of adding protein-protein information to recover a protein-protein network?
Additionally, the performance in recovering a known network from different amounts of synthetically generated data is evaluated.
Second, I recover a negative relationship between network size and heritability.
Because of the first order Markov assumption underlying our method we expect, that all of the edges in Figure 5(b) can be recovered by a network reconstruction based on time series data.
The benefit of the obtained 3D information is that distribution of the network in 3D space allows the use of prior knowledge, such as assuming allometric relations in a branching structure (Aiteanu and Klein, 2014), to recover an approximation of the network.
