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Semantic technology company SciBite has announced 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.

SciBiteAI 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. SciBiteAI has been designed to meet key needs for the life sciences with three guiding goals: to enable scientists, researchers, and application developers to use semantics-based deep learning without having to become machine learning experts; to replace complex coding with standardized REST APIs, ready for integration into business workflows and software; and combine SciBite’s expertise in semantics and FAIR data to develop machine learning-based solutions that enhance the understanding of scientific content.

SciBiteAI 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: language comprehension based Named Entity Recognition (NER) to identify concepts not covered by existing vocabularies or ontologies; integration with SciBite’s TERMite NER software to improve disambiguation and term discovery; relationship identification; and Q&A.

SciBiteAI’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.

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