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 resource from busy specialised data science departments to refine and redo highly similar ad hoc searches. The result of this is 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 endeavours.
SciBite releases a new version of industry leading, ultra-fast named entity recognition (NER) and extraction engine, TERMite 6.3, which delivers a range of new enhancements, including simplified connectivity to third party systems.Read
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
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