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This phenomenon can be explained by the definition of maximum cohesion function, which takes into account all pairwise closeness between merged terms.
Correspondingly, we define function g(T1, T2,…, T k ) = max T 1,2,…, k (∑ X ∈ V (T 1,2,…, k ) weight(X)) as the maximum cohesion function for integrating the ontologies T1, T2,…, T k.
Correspondingly, we define function g(T A, T B ) = max T AB (∑ v ∈ V (T AB ) M AB (v)) as the maximum cohesion function for integrating the ontologies T A and T B and its value is the maximum overall cohesion score (or, simply, maximum cohesion score).
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Therefore, we propose that the replication checkpoint and chromosome cohesion function in separate pathways.
In combination, these results reveal that Ctf7p cohesion function is greatly influenced by Elg1-RFC levels.
Therefore, these results strengthen the fact that the cohesion function of Swi3 is defective in swi3-E31 and E39.
The pseudocode for calculating the maximum cohesion score is described in Algorithm 2, which visits ontology vertices in reverse topological order when filling up the cohesion matrix.
At the end of Algorithm 2, the cohesion matrix is filled up with optimal cohesion scores and the maximum cohesion score is saved at entry (0,0), as described by Theorem 4. Theorem 4. It holds that cohesion _ matrix (i, j ) = g (T A − i, T B − j ) and cohesion _ matrix (0,0) = g (T A, T B ).
We proposed a novel ontology integration problem that optimizes the cohesion function.
The chromatin rescuers all appear to have positive effects on cohesion function.
By studying the maximum cohesion scores between ontologies under a graph setting, we identified an approximation structure and developed an approximation algorithm for integrating multiple ontologies.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com