Making true “molecule”-“mechanism”-“observation” relationship connections is a time-consuming, iterative, and laborious process. In addition, it is very easy to miss critical information that affects key decisions or helps make plausible scientific connections.
The current practice for deciphering such relationships frequently involves subject matter experts (SMEs) requesting a resource from busy specialised data science departments to refine and redo highly similar ad hoc searches. The result of this is an impairment of both the pace and quality of scientific reviews.
In this presentation, you’ll hear from GSK’s Scientific Lead within the Data and Computational Sciences Solutions team, Samiul Hasan, and SciBite’s Head of Data Science, Michael Hughes, on how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making. They will take you through our pilot experiment and highlight practical learnings that should inform subsequent endeavors.
SciBite today announced that GSK Japan, one of Japan’s leading research-based pharmaceutical and healthcare companies, has selected SciBite’s Semantic Platform to enhance pharmacovigilance capabilities and deliver on its commitment to improve the quality of human life.Read
Raw data has the inherent characteristic of being unstructured with potential quality issues such as inaccurate, incomplete, inconsistent, and duplicated. Therefore, it must be processed before it can be used for subsequent analysis and confident data-driven decisions. This is where ontologies come into play.Read
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