In life sciences and biomedical research, public data is essential to the knowledge discovery process. Getting useful results from public data sources however is very challenging: there is too much relevant data and information is stored in too many disconnected data sources. Manually searching all these sources is time consuming; the results are at best incomplete and making the connections between results found is simply impossible. This situation is one of the biggest frustrations of life scientists today.For over 10 years academic data researchers have been trying to address these issues. At the level of manually curated data great progress has been made and clear proof exists that extracting these explicit data manually from sources is highly valuable. However, it is also proven that manual curation is not scalable and thus many scientific discoveries are hidden in the data, waiting to be 'discovered'This presentation will report the Euretos' Bio Relations and Intelligence Network [BRAIN[?]] allowing for high quality in silico knowledge discovery. The platform brings together a wide range of scientifically high value data sources, harmonised in an interoperable format and semantically identified in terms of the concepts they represent The result of this process is a comprehensive network of concepts and their relations derived from all the underlying data sources. BRAIN[?] thus provides one single, accessible view on all underlying data sources involving both the manually curated as well as the computer inferred implicit relations that have either escaped manual extraction or have not been made explicit at all. This allows unearthing 'hidden gems' at an unprecedented scale.

Using the BRAIN, biorelations and intelligence network, for knowledge discovery

248th National Meeting (2014)

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