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Designing active learning algorithms for a crowd-sourced database poses many practical challenges: such algorithms need to be generic, scalable, and easy to use, even for practitioners who are not machine learning experts.
In an age when data is often available with a few keystrokes, the A.T.F. is forced to follow this manual routine because the idea of establishing a central database of gun transactions has been rejected by lawmakers in Congress, who have sided with the National Rifle Association, which argues that such a database poses a threat to the Second Amendment.
Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in.
Indexing of the nr database poses specific challenges as compared with other databases because nr is so large and is updated daily.
In this case, the limited diversity of species and effect types in the ecotoxicity database poses the greatest source of uncertainty.
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As a result, many proteins show no or low sequence match to the known proteins in the database, posing a major difficulty in genome annotation.
Collection of patient information into large databases poses new risks of privacy breaches that did not exist when paper files were simply locked away in file cabinets.
There are a growing number of compound databases, and the dominance of Chemical Abstracts is challenged; however, the mere diversity of these databases poses its own difficulty.
The heterogeneity of data in databases poses a significant challenge to their integration and analysis in practice [1, 2].
However, the heterogeneity of data in such databases poses a significant challenge to their integration and analysis in practice.
However, protecting the privacy and confidentiality of genetic data in an era of electronic health records and genetic databases poses numerous challenges.
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