Biomedical Semantic Resources for Drug Discovery Platforms

  • Ali Hasnain
  • Dietrich Rebholz-Schuhmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)


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.


Biomedical Ontologies and Databases Life Sciences Linked Open Data (LSLOD) 



The work presented in this paper has been partly funded by EU FP7 GRANATUM project (project number 270139) and Science Foundation Ireland under Grant No. SFI/12/RC/2289.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Insight Centre for Data AnalyticsNational University of IrelandGalwayIreland

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