The focus on methods to analyze large databases, ‘big data’, continues to increase as the collections of scientific observations accumulate.  Elsevier has collected tens of millions of facts from scientific literature in the form of semantic triples.  In biology an example triple is “A regulates/causes/changes B” where A and B can be compounds, diseases drugs or other entity types.  The relationship is also qualified by species, tissues, and other variables.  In chemistry the triples are similar, e.g. “compound C inhibits protein A“ and are also qualified by variables such as potency, assay type, species, and variant.   The possible combinations increase factorially with the number of facts joined together by disease, target, or chemical compound.<br/>By combining these observations in biology and chemistry we can explore questions such as “based on the known targets drug A inhibits, what other diseases might it treat, based on disease pathways reported for all other diseases?” and “given the proteins related to a disease, and compounds known to inhibit those proteins what known compounds/structure scaffolds could be tested to treat the disease?”  We will present examples of using data frameworks that combine Elsevier and open source pathway and biological activity databases to explore these questions with the broadest available knowledge base.

Combining semantic triples across domains to identify new and novel relationships and knowledge

251st National Meeting (2016)

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