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Technology
We use advanced Bayesian methods from astrophysics to combine large amounts of research, molecular and patient data, helping us accurately model biological interactions through a foundation model of human cancer biology.
This oncology-specific foundation model uses minimal amounts of data to construct Digital Twins of cell models and real patients for prediction of treatment response and outcomes for existing and emerging cancer drugs.
Dark matter is a property of the entire universe, yet it cannot be measured directly.
To overcome this, astrophysicists infer the unmeasurable properties of dark matter from individual pictures of galaxies.
These pictures are effectively combined into a single unified model to decipher dark matter’s general properties.
Concr teamed up with astrophysicists to adapt these established algorithms for genuine integration of disparate oncology datasets, creating a single holistic model of patient response.
By overcoming the data integration barrier, Concr not only enables powerful analytics, but also provides the critical advantage when working with limited or incomplete data.
Isolated high-confidence results by accurately predicting molecular features most representative of efficacy for therapeutics
300x more data-efficient: 2-3 cell lines were sufficient to achieve same RMSE, compared to 600 cell lines using other methods
Ability to generalise to novel drugs and cell lines across therapeutic classes and indications
Concr modelling can be generalised across to drugs it's 'blind' to
Concr modelling shows early evidence of being generalisable across cancer types
Superior predictive accuracy compared to other methods
7x less patient data required for model training compared to the next best approach
First of its kind: disease-free survival stratification
Concr technology can prioritise measurable features and integrate multiple data types, yielding highly accurate models
Concr models can be used to simulate different treatment scenarios and clinical trial designs to identify optimal treatment strategies
Concr models can identify patient responders at cohort- and individual-level, allowing biomarker identification for best drug response
Our partners leverage Concr advanced predictive modelling across every stage of therapeutic development to create shared value for the benefit of cancer patients.
Our cloud-based platform effectively integrates diverse data parcels to generate meaningful insights.
