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Pearson Product Moment Correlation coefficient analysis was used to detect relation between age, body mass index, height, weight, T scores and endothelin levels.
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The presented RE approach is well suited to detect relations expressed as predicate – argument structures while the detection of relations expressed within nominalizations and syntactically more complex relational expressions remains a challenge.
The goal of relation extraction is to detect relations between two entities in free text.
We thus had a large number of events, increasing our power to detect relations.
This means that the power to detect relations between these factors and sickness absence was limited.
Subsequent incorporation of partial records through application of the EM algorithm gives us greater power to detect relations.
Recently, we have reported an extension of this system to detect relations between drugs and their effects [ 33].
To increase the power to detect relations as a result of these models, we created two SWED-QUAL composite scores (PCS and ECS) through a data reduction procedure.
To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined.
However, the average cross-correlation from several cells was much weaker, showing that single-cell approaches such as the one presented herein are useful to detect relations between proteins that vary in individual cells.
For all annotated interactions in both test corpora, a detected relation was considered as true positive (TP), if the actor was mentioned within the ARG0 part, the target was mentioned within the ARG1 part and the verb of the corresponding PAS resembled the meaning of the annotated relation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com