Sentence examples for mutual information analyses from inspiring English sources

Statistical coupling, residue correlation and mutual information analyses along with clustering were applied to analyze the structure-based multiple sequence alignment of the catalytic domains of the Ser/Thr PK family.

Critically, results from both classifier and mutual information analyses demonstrate that the full network architecture of the model provides an efficient way of boosting the encoding capacity of neurons in a way that bears remarkable similarities to actual neurons from primate auditory cortex.

Non-linear functional connectivity can be established by means of mutual information analyses that (i) describe the amount of information in one area given the time series information in another area; and (ii) are sensitive to static non-linear dependencies (Roulston, 1999; David et al., 2004).

To quantify how much each of the GroEL paralogs has diverged not only functionally at particular sites but also in their evolutionary paths from GroEL1, we applied mutual information based coevolution analyses (see methods for details) between GroEL1 and GroEL2 and GroEL1 and GroEL3.

In this study, two different input selection methods were used: cross-correlation analysis (CCA), and a combination of mutual information and cross-correlation analyses (MICCA).

Potential relationship between these genes was modeled by using nonlinear correlation analyses, mutual information associated to significance analysis [ 11, 12], which are proven to be more accurate than linear statics techniques even if the latter are simpler to implement [ 13– 17].

To achieve reliable multi-step-ahead prediction, the optimal architecture of networks is determined by average mutual information and false nearest neighbors analyses in chaos theory.

Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.

The square of the Pearson correlation coefficient was used as the measure for sensitivity in the first of the analyses, and the mutual information was used as the measure in the second.

Computer network traffic is analyzed via mutual information techniques, implemented using linear and nonlinear canonical correlation analyses, with the specific objective of detecting UDP flooding attacks.

The mathematic tools consider simple correlation analyses (Pearson, Spearman) [ 336, 337], mutual information [ 337], linear regression [ 338– 340], regularized least squares [ 341, 342] and bayesian inference [ 343, 344].