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Our results show that the model trained on the fully-automatic database achieved high overall accuracy (94.12%), and that a statistically significant improvement (to 96.30%) can be achieved by manually annotating a specific part of the database.
Using these functions as reference, our predictions with and without V. alfalfae proteins in the database achieved an F1 Score of 89% and 88%, respectively.
The ARM database achieved better performance than the conventional database in three of the five data-retrieving tests, but was less efficient in the remaining two tests.
Analyses were based on a national linked database, achieved by probability matching and comprising acute hospital discharge records, psychiatric hospital records, cancer registrations, and mortality records (Kendrick and Clarke, 1993).
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The database achieves this by using a cache for write operations (updates are immediately written to a logfile, then cached in memory and only later written to disk, making the insertion process itself faster).
Latanya Sweeney proposed a measure that is k-anonymity [200], defining that a database achieves k-anonymity if for any tuples of previously defined entries of the database, there are at least k users corresponding to it, making it impossible to re-identify a single user with only information on these entries of the database.
Both databases achieved a prediction accuracy of 71.8%, just below the prediction of psipred as implemented by the Phyre2 server (72.7%).
Most databases achieve high score values for correct orthology predictions.
The communication between server and the database is achieved using Java Database Connectivity API (JDBC).
The classifier was built with linear discriminant analysis and the results imply that the selection of potential biomarkers in database 1 achieved perfect linear separability in database 2. This provides solid evidence on the competitiveness of the proposed method.
We tested the proposed method on our patient database and achieved a high classification accuracy.
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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