Cambridge, UK – SciBite, an Elsevier company, and award-winning semantic technology company, today announced the release of SciBiteAI 2.0, a framework for leveraging AI and deep learning models alongside semantic technologies to unlock insights into Life Science data.
SciBite’s AI framework enables you to:
At the heart of SciBiteAI are our series of dynamic deep learning Named Entity Recognition and Relationship Extraction models, built from a combination of our industry leading semantic technology, proprietary pipeline methodology and specialist training data. These perform a wide variety of functions:
SciBiteAI 2.0 includes new architecture with major speed and scaling improvements, providing users with a suite of language comprehension models including:
“SciBiteAI combines the context and language capabilities of machine learning with the NER algorithms of our expert curated vocabularies to create a host of new opportunities for our customers’ data”, says Product Manager, Andy Balfe. “From identifying novel connections, building custom ontologies to clustering data, our technology can help.”
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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.
SciBite announces the release of SciBiteAI Relationship Extraction models, which provide the enhanced ability to identify complex relationships within text to further unlock insights from Life Sciences data.Read
SciBite announces the launch of SciBiteAI, a state-of-the-art Artificial Intelligence software platform for leveraging machine learning models alongside semantic technologies to unlock insights into Life Sciences data.Read
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