Data for drug discovery and healthcare is often trapped in silos. This hampers effective interpretation and reuse. An antidote is to link data both internally and externally and create a Findable, Accessible, Interoperable, and Reusable (FAIR) data landscape that can power semantic models and knowledge graphs.
Data for drug discovery and healthcare is often trapped in silos. This hampers effective interpretation and reuse. An antidote is to link data both internally and externally and create a Findable, Accessible, Interoperable, and Reusable (FAIR) data landscape that can power semantic models and knowledge graphs.
Data generation for drug discovery begins with the identification of targets and finishes, often over a decade later, as a submission to a drug regulatory authority such as the Food and Drug Administration (FDA) agency. Across the various stages in this process, data is often identified with inconsistent identifiers, various names for the same entity, and it is not linked. Consequently, interpreting and reusing the data effectively is a challenge.
Effective interpretation and reuse of data can be enhanced using knowledge graphs. Knowledge Graphs provide a means to link data and metadata both internally, and to external sources following Findable, Accessible, Interoperable, and Reusable (FAIR) data guidelines.
Knowledge graphs start with subject matter experts working with data engineers to capture understanding through concept models. These are combined with relevant ontologies, which define concepts and the relationships between them to create semantic models using, for example, the Resource Description Framework (RDF). They contain Uniform Resources Identifiers (URIs) for the data to provide a formal representation of meaning which machines for interpretation and analysis at scale can read.
Knowledge graphs are a dynamic store for diverse sources of data and metadata, their concepts, and relationships as nodes and edges in a graph and semantics for encoded or inferred meaning. Linked data and metadata are more likely to be FAIR. Besides these advantages, knowledge graphs can be used in combination with machine learning algorithms for powerful analysis that can include semantics.
Furthermore, knowledge graphs can be used to structure and link together large amounts of data. By connecting data, knowledge graphs allow users to make informed decisions based on all the available information and find connections that might not have been found otherwise. In sum, knowledge graphs provide the framework, which can make drug discovery much more efficient, effective, and approachable.
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