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After both linear and non-linear analyses are completed, statistically significant miRNAs (P < 0.05) are plotted as heat maps using hierarchical cluster analysis and Euclidean distance matrix computation methods.
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Results of the small-scale case are calculated and compared by the discernibility matrix and the matrix computation to verify the new method we study in this paper.
When involving practical computation, convenient reference coordinate systems can be chosen and the methods and tools for matrix computation can be employed.
One approach that relies on computation methods to design the matrix compositions of the ductile phase for optimum fracture resistance and ductility is highlighted.
CompPhy gives access to two SUPERTREE COMPUTATION methods: PhySIC_IST [ 32] and Matrix Representation with Parsimony (MRP, [ 33, 34]).
This paper presents a direct and a sensitivity method for the parameter covariance matrix computation.
In this paper, we propose a generic method of automatic parallelization for sparse matrix computation.
A method for constructing Poincaré projections based on the join probability matrix computation is provided.
Lecture 18: Covariance matrix computation, optimization, review.
Lecture 17: Distributed singular value decomposition, covariance matrix computation.
Preliminary version in Distributed Matrix Computation with NeurIPS14.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com