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In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases.
The results are based on a generalization of the Rice formula.
The proof is based on a generalization of div curl lemma for transient problems.
The theory is based on a generalization of thin shell theory and the Complementary Strain Energy Principle.
A new fallback rule based on a generalization of an integer cut for integer parameters is presented.
These results hinge on a generalization to Banach modules of Tomiyama's famous theorem on contractive projections onto a C∗-subalgebra.
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Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network.
The existence of the function g in the expression of ∂ ∂ l leads to a generalization on conditions for the solvability of integral geometry problems.
Results seem to suggest that participants in the comparison condition focused on abstracting a generalization from the two proverbs without encoding each proverb deeply.
However, due to the limited number of studies performed on each EST fraction separately, a generalization on the relative abundance of SSRs across those fractions warrants further investigation.
We develop a numerical algorithm based on Isogeometric Analysis, a generalization of the finite element method that permits the use of globally-smooth basis functions, leading to a simple and efficient discretization of higher-order spatial operators in variational form.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com