Oculair.KE™ uses statistics and AI to evaluate complex biological systems comprising scientific (genomic, drug and protein systems) real world (phenotypic and image data) and other contextualising (environmental, social etc.) data sets in order to abstract relationships between seemingly unrelated pieces of information.
The ability to identify and validate new clinical hypotheses, may help to redesign and improve patient pathways; to identify new uses for approved drugs, to stratify patient populations and identify clinical endpoints to make clinical trials more efficient; to identify relative value for money between new/existing products and pathways; to identify biomarkers that may help diagnose patients sooner; and to predict drug response or adverse events enabling personalised treatment.
RAIR Knowledge Graph
Oculair.KE™ solutions use proprietary data, expertise and state-of-the-art AI to discover, interact and draw insight from a vast store of structured and unstructured life science knowledge.
The RAIR Knowledge Graph ("RKG") comprises a hyperlinked graph database of over 1.4m entities and 10's of millions of relationships curated from diseases that manifest in the eye, including macular degeneration, glaucoma, Alzheimer’s disease, chronic kidney disease, rheumatological disease and cardiovascular diseases. RKG semantically connects public-domain databases with research abstracts to find hidden relationships between widely disparate data. This multi-omic, big-data tool can be used to generate hypotheses for drug repurposing, to identify disease biomarkers, and to illuminate novel treatment pathways.
Knowledge Graphs -- Powerful Big-Data Technology
The graph technology that underlies RKG allows semantically-rich databases to be integrated in a conceptual and flexible way. RKG has an in-built automated deductive reasoning engine that does not require labour-intensive feature engineering. And rather than being a black box, RKG is able to fully explain its reasoning, thus providing results that are traceable and understandable in human terms. Relational data models are often constrained because they store information as “strings” in row/column relationships, making them unsuitable for modelling complex knowledge domains. RKG’s data model solves this by storing information as concepts and allowing n-relational connections. This hyper-relational schema gives RKG the ability to understand complex queries and provide powerful insights as results. Together these make RKG the perfect tool for research ideation and the formation of hypotheses for the purposes of AI-enabled development.
What can Oculair KE do for you? Please check out our website or fact sheet or get in touch with us today so that we can set up a demo.
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