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A major drawback of these interactive systems is that they still cannot be considered to be practical tools for a massive database annotation: for instance, consider a case in which a reduction of the 50% of the amount of interactions is achieved; if 1000 onsets had to be annotated, the user would have to deal with 500 elements, which still constitutes a significant workload.
In contrast a narrow and highly reliable set of predicted interactions is achieved using lower cut-off values.
Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.
This paper investigates two strategies in which model development with interactions is achieved using a single data set generated from the Expectation Maximization (EM) algorithm.
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Various couplings are proposed where interactions are achieved through the causal influence between state variables.
These kinds of highly oriented molecular interactions were achieved by utilizing the structural property relationship of nanostructured materials in-lieu with their synthetic strategy.
The best interactions are achieved in P4HS/PCL and the bromination or methoxylation of the P4HS worsen the interactions with the PCL.
Cross-layer interactions are achieved through joint, traffic-dependent adaptation of the backoff probability at the MAC layer and the modulation order at the physical layer.
The same extensive hydrogen bonding, hydrophobic contacts and van der Waals interactions are achieved by binding of oligomannose-3 in both FimH RBDs of the crystal structure (Figure 2).
How are these regions making contact with each other, and how can the specificity in such long-range interactions be achieved?
Quantification of the kinetics of diVCA-Arp2/3 and Arp2/3-filament interactions was achieved by fitting distributions of bound and dissociated intervals to one-, twor, or three-exponential functions, as described previously (Smith et al., 2013).
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