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According to the scope of data coverage, biological databases can be classified as comprehensive and specialized databases.
Through the use of this administrative site, new data gathered from different biological databases can be uploaded to the server.
Using InterMine, large biological databases can be created from a range of heterogeneous data sources, and the extensible data model allows for easy integration of new data types.
In this paper, we argue that universal and ad hoc access to community biological databases can be facilitated through natural language interfaces to counter interface constraints.
Considering the heterogeneity in data type, scope and curation, biological databases can be classified into multiple categories under different criteria as presented above, making it easier for people to effectively characterize databases and identify the database(s) of interest.
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NLP-derived databases can be useful in addressing significant biological questions, such the as identification of protein interaction networks that are common to disease-pairs.
Finally, several tools and databases can be used to functionally interpret the modules, to analyze what biological functions and pathways are enriched within the modules or to find whether the module could be associated with certain diseases.
A compilation of these databases can be found on Pathguide a meta-database of more than 190 biological pathways and network databases [ 12].
A comprehensive list of the omics databases can be found by searching the internet with the term "list of biological databases".
To this end, the exploitation of the current wealth of 'omic'-scale databases can be facilitated by automated approaches, allowing researchers to directly address specific biological questions.
At the simplest level, two databases can be related to each other when they both reference a common set of biological variables.
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