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This digraph is translated into a one-to-one matrix known as the Maintainability Permanent Matrix (MPM) and based on this matrix; the Variable Permanent Function for Maintainability (VPFM) is proposed, which is characteristic of the Mechanical system.
Based on this matrix of proximity between the samples, one can construct a similarity diagram called a dendrogram.
Positions and movement lines are plotted on this matrix.
A machine learning model is trained on this matrix.
Based on this matrix, overarching themes were constructed in conversations between the first and second author.
A machine learning model was trained on this matrix, irrespective of the algorithms used to generate the model library.
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Clustering was performed on subsets of this matrix selected based on cumulative probability thresholds, where the cumulative probability was calculated as the product of the probabilities for each compound.
The numerical solution for λ 1 ∗ based on the DBFDA of K ∼ ˙ T is less accurate than the one based on the LBFDA of this matrix.
On the basis of this matrix [0,1] we performed a principal coordinate (PCO) analysis to suggest relationships between genomes [ 63].
We applied hierarchical clustering on the columns of this matrix, in order to regroup co-expression clusters showing similar predicted regulatory motifs.
Based on this matrixing network, distributed computing was employed in solving the huge mathematical models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com