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We also proposed a high-precision rule-based PPI extraction method and showed our method achieves substantially higher precision than two state-of-the-art PPI extraction baselines in both per-relation and per-instance evaluation.
Our approach achieved the highest precision in both per-relation and per-instance evaluations.
The performance of the baselines was improved in both per-relation and per-instance evaluations as we pipelined the negatively-labeled instances to our rule-based system.
As shown in Table 2, our rule-based approach exhibits the highest precision in all groups in both per-relation and per-instance evaluations.
Second, current PPI research evaluates their performance on a "per-instance" basis.
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances.
According to the conventional per-instance basis evaluation, 100% accuracy is achieved only when a PPI tool correctly labels all the 8 positive instances as positive and the remainders as all negative.
Most previous Protein Protein Interaction (PPI) studies evaluated their algorithms' performance based on "per-instance" precision and recall, in which the instances of an interaction relation were evaluated independently.
Wage concessions extorted from labor may increase short-term profits on a per company instance but reduce business in the macro economy in the same economic moment.
There were 443,411 unique ordering instances, leading to an average of 1.99 add-on tests per instance.
Of the 880,359 add-on orders, there were 443,411 unique ordering instances, leading to an average of 1.99 add-on tests per instance.
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