Sentence examples for matrix of true from inspiring English sources

This correlation is computed as ρ G S I, H = w ′ Γ ^ w w ′ C w, where C is the covariance matrix of true breeding values (Equation (1)) and Γ ^ was obtained according to Equation (7).

In each selection cycle, we used the restricted maximum likelihood method (Patterson and Thompson 1971) to estimate the covariance matrix of true breeding values (C) and of the residuals (R), which were denoted as C ^ and R ^, respectively, from where matrix P ^ = C ^ + R ^ was an estimator of the phenotypic variance-covariance matrix (P).

The i th observation is augmented with one success and one failure, both carrying weight h i / 2, where h i is the i th diagonal element of the weighted hat matrix W 1 / 2 X (X T W X ) − 1 X T W 1 / 2, W is the diagonal matrix of true variances which depends on the parameters β, and X is the design matrix.

In principle, ( hat{T} ) can be seen as a proxy transfer matrix of the true transfer matrix T to perform decoding.

Table  2 shows a matrix of the true positive mutations which were identified in the thermotolerant strains by NGS and validated with Sanger sequencing.

In order to assess the performance of different matrices in terms of true and false positives, we used the method adopted by Brick and co-workers [ 11] (described in Methods).

In addition, a confusion matrix, which provides a breakdown of true positives, false positives, true negatives and false negatives, is shown for each disease.

We have also compared the performance of all the matrices considering specificity as proportion of true hits among all positive predictions using ROC curve.

We simulated aberration profile matrices; in each simulation, we obtain a pair of true and false positive aberration matrices.

Splice variants are not only unutilized but also complicate the detection of true paralogs for phylogenomic matrix construction.

Then, the integration in Equation (8) can be performed to yield the approximate overlap (10) where Here, m = { A, B} represents the model index, the -dimensional true mean and the true variance-covariance matrix of the corresponding model response PDFs, which both depend on the model structure m, measurement time point t, stimulus u(t) and readout selection.