Graph & Reasoning Engine Platform
Our graph engine and software platform integrates any data outputs or model outputs into a graph structure, from biological entities to clinical outcomes.
Graph Integration Platform
Our core technology is based on multimodal graph reasoning techniques. It is underpinned by a graph database engine and a hybrid AI utilising automated reasoning. This allows us to integrate any and all data layers with external knowledge sources and other models, informing the analysis of complex clinical and biological data.
This approach to data analysis integrates layers of data in a graph, where they can be visualised and from which point automated reasoning techniques can be applied. The AI produces a fully explainable set of recommendations by reasoning on given patient biologies, and can be used in contexts ranging from cell biology to spatial imaging to individuals to whole patient groups. Ultimately, the AI uses patient biology to explain clinical outcomes, and simulates the impact of a drug or drug combination on biological states.
Multilayer Graph
Our platform lets you build multi-layer graphs and manage multiple graph databases in parallel to connect different types of data. The multi-layer graphs can represent different biological and clinical entities, features, and data points from your experiments, clinical trials, public datasets, and our real-world datasets.
Spatial Interactions Simulations
We leverage our high performance graph engine to simulate spatial interactions based on spatial biology data and imaging, such as tumours or biopsy pieces from patients. We use this approach to generate a digital twin of the tumour to identify spatial targets and simulate drug response in the tissue.
Multimodal Spatial Biology
Identify complex biological circuits, then create spatial simulations of them with our spatial biology module.
The platform can analyse complex interactions to identify targets in tissues, simulate and predict potential biological response to drugs in individuals and cohorts, and test and prioritise new drugs and combinations in-silico based on spatial biology.
