Cambridge, UK – SciBite, the award-winning semantic technology company, today announced the launch of SciBite AI, a state-of-the-art Artificial Intelligence software platform for leveraging machine learning models alongside semantic technologies to unlock insights into Life Sciences data.
SciBite AI offers capabilities beyond the plethora of other AI solutions in the market by combining machine learning with SciBite’s established industry-leading ontology-based semantics, enabling customers to unlock insights hidden in the mountain of life science text. SciBite AI has been designed to meet key needs for the life sciences with three guiding goals:
SciBite AI was created using state-of-the-art deep learning language models, trained with data leveraging SciBite’s industry-leading semantic technology and curation. The platform offers a wide variety of functions, including:
SciBite AI’s architecture is designed to remove the need to write complicated code, ensuring it is readily deployable for applications. The solution is also customised for scientific text, ensuring it is optimised for use in the life sciences, often a weakness of more generic tools.
“SciBite AI represents the next generation in our ability to understand and analyse scientific text,” says SciBite’s CTO, James Malone. “Our software now exploits and helps build our life science ontologies, as well as find novel and relevant relationships within data. With SciBite AI we offer a complete solution from managing core data standards to advanced AI-based discovery.”
Learn more at Scibite AI
SciBite is an award-winning semantic software company offering an ontology-led approach to transforming unstructured content into machine-readable data. Supporting the top 20 pharma with use cases across life sciences, SciBite empowers customers with fast, flexible, deployable API technologies, making it a critical component in data-led strategies.
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SciBite's CTO James Malone explains how the semantic approach to using ontologies is essential in successfully training machine learning data sets. In this blog he discusses how Sherlock Holmes (amongst others) made an appearance when we looked to exploit the efforts of Wikipedia to identify articles relevant to the life science domain for a language model project.Read
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