Quantifying the Content of Biomedical Semantic Resources as a Core for Drug Discovery Platforms

The biomedical research community is providing large-scale data sources to enable knowledge discovery from the data alone, or from novel scientific experiments in combination with the existing knowledge. Increasingly semantic Web technologies are being developed and used including ontologies, triple stores and combinations thereof. The amount of data is constantly increasing as well as the complexity of data. Since the data sources are publicly available, the amount of content can be measured giving an overview on the accessible content but also on the state of the data representation in comparison to the existing content. For a better understanding of the existing data resources, i.e. judgements on the distribution of data triples across concepts, data types and primary providers, we have performed a comprehensive analysis which delivers an overview on the accessible content for semantic Web solutions (from publicly accessible data servers). It can be derived that the information related to genes, proteins and chemical entities form the core, whereas the content related to diseases and pathways forms a smaller portion. As a result, any approach for drug discovery would profit from the data on molecular entities, but would lack content from data resources that represent disease pathomechanisms.