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Transcription factors are important controllers of gene expression and mapping transcription factor binding sites (TFBS) is key to inferring transcription factor regulatory networks.
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Although we are aware that the intensive sampling described here may not always be feasible, we would like to note that including field epidemiological information to guide the computational algorithms used to infer relationships among populations was key to the success of the analysis and should become a common practice when attempting to reconstruct the invasion history of an exotic organism.
Geometric variation model is introduced to infer key parts and key features, helping forming tolerance specification while assuring rationality and sufficiency of structure decomposition.
The observed correlations between any two traits in addition to the correlations between these traits and genotypes at the given locus, are the key ingredients to inferring the most likely relationship between the traits.
Addressing this key question is essential to infer the implication of peripheral clonally expanded CD8+ T cells in the disease.
In the former case, the key concern is to infer an attractor structure close to that of the true network.
The mapping of TFBS is important to infer the regulatory networks of transcription factors (TF) which are key controllers of gene expression.
To imply is to suggest; to infer is to conclude.
As noted by Raucci et al. ([29]p.157), "when a stratigraphy is constructed without comprehensive mapping, the tendency is to infer the distribution of key intervals based on correlations between stratigraphic columns, without fully confirming these correlations".
GraphAlignment differs from the above approaches [ 11, 21- 27] by two key features: (a) An explicit model of network evolution is used to infer alignment parameters from the data.
A systematic approach for the semantic integration of relevant information is a key requirement to infer reliable monitoring states of the devices.
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