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Exploiting the Life Science Data Explosion to Speed New Drug Discovery

r to “normalize” the form, enabling integration of different sources in a seamless, ad-hoc manner. This provides researchers and bioinformaticists with a simplified view of the data, and eliminates the need for intermediate data stores. A coherent data integration platform that is agnostic to the underlying “store” enables such a shift: for example, allowing simultaneous access to local (proprietary) sources, legacy systems and up-to-date public sources from a central, mediated interface. The ability to leverage existing systems into more scalable solutions, with incremental deployment, greatly reduces the risk of adopting new systems and can, as we will demonstrate, have a dramatic impact on drug R&D productivity.


The Solution Space
Traditional Solutions
Traditional data integration solutions depend on a great deal of “custom coding” to create local data silos of various forms. Each approach has pros and cons, but all share some inherent limitations and typically do not scale well. In general, scientists and bioinformaticists have been forced to hand code data access and integration tools in order to conduct scientific queries for their research. This vertical approach has created huge IT inefficiencies which slow the discovery process. Further, the result is a limited database that rapidly becomes out of date as data sources change and expand. Issues of data staleness, consistency, lack of flexibility and ultimately performance limit the growth of such approaches.

Traditional relational and object database systems require compromises in the structure of the data model, and source data often does not fit into these structures naturally. Emerging database technologies, such as XML databases, address some structural limitations, but imply a huge conversion investment which still results in huge local silos that share the limitations inherent in
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