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For simplicity, users see the full catalog of matrices organized into 346 distinct TFs, whereby each one may represent occurrences for several matrices that predict the same class of TF.
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GDM is a matrix regression technique that predicts biotic dissimilarity across the landscape based on the matrix correlation between biotic dissimilarity and environmental dissimilarity plus geographic distance between sites where the species has been sampled.
GDM is a matrix regression technique that predicts biotic dissimilarity (turnover) between sites based upon environmental dissimilarity and geographic distance.
To predict the distribution of environmentally associated genetic and phenotypic variation across the landscape, we used GDM (Ferrier et al. 2007), a matrix regression technique that predicts biotic dissimilarity (i.e. beta-diversity) between sites based upon environmental dissimilarity and geographic distance.
This simpler demographic model is based on a projection matrix that predicts the essential dynamics of growth in the vertical dimension.
Additionally, the results indicate that matrix failure takes place earlier than that predicted by von-Mises failure criterion and that the 1st Stress Invariant criterion can better predict matrix failure under tensile loading.
We would want to have a model selection method that enables to select the adjacency matrix that best predicts the true topology.
PSIPRED is a two-stage neural network that predicts protein secondary structure based on position-specific scoring matrices [ 27].
Each coexpression matrix in the aggregated coexpression matrix was first normalized so that edges are retained only if the nodes exhibit a correlation above that predicted by their node degree alone (e.g., as in [67]).
Note that the prominent diagonal line in each matrix shows that predicted trial type matched the actual one in most cases for all rats.
The results were subsequently used to build a model with a position-specific scoring matrix (PSSM) that predicts the selectivity of the PDZ domain [ 97, 99].
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