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We parameterize the gain matrices using the LMI conditions.
Resulting matrices were compared with FlyReg matrices using the Kullback-Leiber distance [17].
The results varied for the different matrices using the three different cones.
We generated position weight matrices using the sequences from the top ten enriched motifs.
Principal Component Analysis was performed on these matrices using the prcomp function.
Under this formulation, Lo et al. [ 1] have shown how to compute efficiently both the genetic covariance matrix using the tabular method [ 14], and its inverse using the algorithms of Henderson [ 15] and Quaas [ 16].
The next step is to connect the IC to each matrix using the correct circuit.
We can also use the variance-covariance matrix using the assumption of normality (p. 134, [49]).
Trees chosen for the supertree computation were coded into a binary matrix using the "matrix representation using parsimony"(MRP) method as implemented in Clann software (version 2.0.2) [59].
Consider the discretized matrix A obtained from matrix A' using the alphabet Σ.
Consider now the discretized matrix A obtained from matrix A' using the alphabet Σ.
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