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Exploiting the Life Science Data Explosion to Speed New Drug Discovery

............................................... 9
Relational Approach ............................................................................................................................. 9
Nested Relational Approach ..............................................................................................................10
Example: Using Public Data, while preserving privacy ...................................................................11
Data Integration across private / cached public sources .....................................................................12
Summary and Conclusion ........................................................................................................................12


Introduction
Life science researchers are experiencing a “data explosion” due to vast amounts of raw data being produced from both public sources and from high-throughput sequencing and other industrial-scale technologies being utilized in-house. This exponential growth curve is expected to continue. In fact, genomic data alone is now doubling every 12 months according to UBS Warburg. From a scientific perspective, this wealth of data creates exciting and unprecedented opportunities for new drug discovery. But being able to quickly and effectively turn this data into knowledge has proven to be a serious challenge and a concerting drain on drug R&D productivity.

Ironically, huge gains in efficiency in the “front end” of the discovery pipeline have created huge “down stream” inefficiencies because the data cannot be accessed, integrated, and analyzed quickly enough to meet the demands of drug R&D. The industry has outgrown traditional proprietary data capture and integration methods, and traditional
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