Sentence examples for most probable network from inspiring English sources
So too for the maximum posterior network — the single most probable network in a chain — unless its probability mass dominates a possibly multi-modal landscape, comprising a near-infinity of alternative models, its status as a representative estimator is questionable [50].
The expression is suitable for engineering application for PC clusters up to 200 PCs per phase and for the most probable network connection of the cluster.
The notion of the most probable network structure is made formal by the Bayesian score criterion, which is simply the posterior probability of G given X: (1) P (G ∣ X ) = P (G, X ) P (X ) ∝ P (X ∣ G ) · P (G ), (2) log P (G ∣ X ) ≅ log P X ∣ G∣ G ) + log P (G ).
To address the signaling pathways differentially activated in ASCmo, the validated gene expression data was analyzed in silico with the IPA software to obtain the most probable networks and biological pathways.
During these estimation and learning processes, the PPI interaction mathematical model can derive the most probable PPI network for cancer and normal patients from large amount of microarray data and big databases to interpret the hidden biological mechanisms.
Using the scores of the genes and the reliability weights of interactions in the starting molecular network, TimeXNet identifies the most probable paths within the interaction network connecting the initial response genes to the late effectors while incorporating the intermediate regulators.
In order to decide the most probable of cross-linking network alternatives mentioned in FIG. 4 we used the unrefined docking model obtained using crystal structures of PTP-SL and ERK2, by positioning the phosphotyrosine of ERK2 lip into the active site of PTP-SL (see also [14]).
To this end, we counted the number of mutations connecting a haplotype with the most probable root of the network as inferred by TCS as a measure of relative age.
However, the fact that haplotype LUZ-40 emerged as the most probable outgroup in the network, and the significant negative value of the Fu's F s, and Tajima's D statistics seem to better support the range expansion and colonization scenario.
We focus on two most common optimization problems in graphical models: finding the Most Probable Explanation in Bayesian networks and solving Weighted CSPs.
In experiments, where most probable explanations in Bayesian networks are computed, we use synthetic problem instances as well as problem instances from applications.
