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Ontology Management
Harmonise technology through scientific ontologies adhering to public standards, whilst curating vocabularies
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Enterprise FAIR Data
The challenges of harmonising data to be Findable, Accessible, Interoperable and Reusable
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Text Analytics + Semantic Enrichment
Transform previously unusable text to data in a richly annotated, machine-readable and standardise data formats
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Semantic Search
Semantic search, visualise results, integrate into your existing platforms and automate your workflows
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Knowledge Graphs
Semantic enrichment technology that facilitates the production of knowledge graphs
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Artificial Intelligence
Combine deep learning and semantic algorithms to build powerful models that can exploit life science data and accelerate its use in R&D
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Why choose SciBite?
At SciBite, we're passionate about helping our customers get more from their data. We love what we do and we think you'll love working with us, here's why...
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Built by scientists for scientists
Our suite of semantic solutions is the culmination of the perfect storm of tens of years of experience
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Unique deep learning approach
Transforming previously unusable but scientifically relevant textual content into machine-readable clean data
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Putting customers at the heart
Our philosophy is to listen, engage & work together with our customers to make ground-breaking achievements
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Supporting the top 20 pharma
Our customers are utilising higher quality data, integrating more data much faster with greater accuracy
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News and opinion
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Functional foods: Revolutionizing health through diet and innovation
Embark on a journey through the transformative realm of functional foods, where the convergence of nature's wisdom and cutting-edge innovation is reshaping our approach to health. In the 21st century, these foods emerge as powerful allies, combating lifestyle diseases and ushering in a new era of well-being.
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What is Retrieval Augmented Generation and why is the data you feed it so important?
Within the life sciences, evidence-based decision-making is imperative; wrong decisions can have dire consequences. As such, it is vital that systems that support the generation and validation of hypotheses provide direct links, or provenance, to the data that was used to generate them. But how can one implement such a workflow?
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Unlocking important RWE from patient data (Part 3) – Can we find all the relevant patients?
In our final installment of this series, we demonstrate how to extract a relevant subset of patients from the simulated data using two approaches – one using SciBite tools and one without.
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