Cambridge, UK – SciBite, the award-winning semantic technology company, today announced the launch of SciBite Ontology Services – a cost-effective and efficient solution for Life Sciences organisations to flex the capacity of their data science team and extend their capabilities by leveraging SciBite’s expert ontologists and biocurators.
Clean data is necessary for next generation computational approaches, such as Artificial Intelligence, to deliver accurate results. Well curated ontologies provide the foundation to contextualise unstructured scientific text and enable it to be transformed into clean, ‘self-describing’, machine-readable data. However, the creation and maintenance of such ontologies is usually limited by the availability of internal data science resources.
“SciBite’s Ontology Services team have the unique and valuable combination of extensive industry experience, coupled with unparalleled expertise in ontology development, standards and biocuration” says Jane Lomax, Head of Ontologies at SciBite. “We’re excited to launch SciBite’s Ontology Services to enable our customers to realise the full potential of the SciBite platform.”
Actively engaged with Life Sciences initiatives, such as Pistoia Alliance, OBO Foundry, ISB and ICBO, SciBite’s Ontology Services team provide thought leadership with a proven track record of delivering significant value across a range of different use cases, including:
Read more about SciBite’s Ontology Services.
SciBite is an award-winning semantic software company offering an ontology-led approach to transforming unstructured content into machine-readable clean data. Supporting the top 20 pharma with use cases across life sciences, SciBite empowers customers with a suite of fast, flexible, deployable API technologies, making it a critical component in scientific data-led strategies. Headquartered in the UK, we support our global customer base through additional sites in the US and Japan.
SciBite CSO and Founder Lee Harland shares his views on why ontologies are relevant in a machine learning-centric world and are essential to help "clean up" scientific data in the Life Sciences industry.Read
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