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Drug discovery is data-hungry and all major pharmaceutical companies maintain extensive in-house instances of public data alongside internal. Analysis and hypothesis generation for drug-discovery projects requires assembly, overlay and comparison of data from many sources, requiring shared identifiers and common semantics. Expression profiles need to be overlaid with gene or pathway identifiers an ...