Sentence examples for contact correlation from inspiring English sources

Analyzing the contacts between the plasmid F specific sequence and the E. coli genome we could identify a high contact correlation between the plasmid and the duplication.

Although different, these methods are common in that they predict the contact map matrix element-by-element, ignoring the correlation among contacts and also physical feasibility of the whole-contact map (physical constraints are not totally independent of contact correlation).

Our method differs from currently popular contact prediction methods in that we enforce a few physical constraints, which imply the sparsity constraint (used by PSICOV and Evfold), to the whole-contact map and take into consideration contact correlation.

A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible.

In order to test whether difference in preference for novelty between groups were related to differences in movement or total object contact, correlations between variables were examined.

VlsE does not obey the contact-order correlation which can be explained in terms of the entropy cost of the size of loops and/or the ordering of residues between contacting residues [18].

Keeping in mind the definition of the above descriptors, the loading profile suggests that high factor scores imply a relatively high density of contacts (positive correlation with REC), an elevated amount of secondary structure (positive correlation with DET) with a well defined characteristic length (negative correlation with ENT).

To this end a contact map, a correlation matrix of Cα positions and a fragment correlation matrix (Γ f) for a window of 4 adjacent Cα atoms were generated for each protein.

Since none of these algorithms utilize structural data (including primary sequence order) and since none of them are based on known signals for contact prediction, any correlation with structural data should arise from their ability to recognize coevolving sites combined with a tendency for coevolving sites to be close together (or for close residues to be coevolving).

Based on self-assessment 10 (12%) had worsened, 14 (17%) were stable, 47 (57%) had improved and 11 (13%) had recovered at contact 2. The correlation between self-rated clinical change between contact 1 and contact 2 and employment status at contact 2 was r=0.54 (p<0.001).

Then we will use weighted generalized estimating equations to properly model the number of contacts taking into account the correlation of contacts recorded by the same participant on two different days.